Overview

Dataset statistics

Number of variables101
Number of observations2000
Missing cells8780
Missing cells (%)4.3%
Total size in memory1.4 MiB
Average record size in memory753.0 B

Variable types

Numeric75
Text17
Boolean9

Alerts

practice has constant value ""Constant
min_IA_ID has constant value ""Constant
start_of_line is highly imbalanced (63.2%)Imbalance
end_of_line is highly imbalanced (68.9%)Imbalance
IA_AVERAGE_FIX_PUPIL_SIZE has 863 (43.1%) missing valuesMissing
IA_FIRST_RUN_FIXATION_% has 863 (43.1%) missing valuesMissing
IA_FIRST_SACCADE_AMPLITUDE has 882 (44.1%) missing valuesMissing
IA_FIRST_SACCADE_ANGLE has 882 (44.1%) missing valuesMissing
IA_LAST_RUN_FIXATION_% has 863 (43.1%) missing valuesMissing
IA_LAST_SACCADE_AMPLITUDE has 878 (43.9%) missing valuesMissing
IA_LAST_SACCADE_ANGLE has 878 (43.9%) missing valuesMissing
Entity has 1725 (86.2%) missing valuesMissing
regression_rate has 863 (43.1%) missing valuesMissing
Unnamed: 0 has unique valuesUnique
reread has 1676 (83.8%) zerosZeros
practice has 2000 (100.0%) zerosZeros
q_ind has 697 (34.8%) zerosZeros
correct_answer has 521 (26.1%) zerosZeros
FINAL_ANSWER has 515 (25.8%) zerosZeros
IA_ID has 22 (1.1%) zerosZeros
IA_DWELL_TIME has 863 (43.1%) zerosZeros
IA_FIRST_FIXATION_DURATION has 863 (43.1%) zerosZeros
IA_FIRST_RUN_DWELL_TIME has 863 (43.1%) zerosZeros
IA_REGRESSION_PATH_DURATION has 863 (43.1%) zerosZeros
IA_FIXATION_COUNT has 863 (43.1%) zerosZeros
IA_SKIP has 767 (38.4%) zerosZeros
IA_DWELL_TIME_% has 863 (43.1%) zerosZeros
IA_RUN_COUNT has 863 (43.1%) zerosZeros
IA_REGRESSION_OUT_FULL_COUNT has 1671 (83.5%) zerosZeros
IA_FIRST_FIX_PROGRESSIVE has 1233 (61.7%) zerosZeros
IA_FIRST_FIXATION_VISITED_IA_COUNT has 882 (44.1%) zerosZeros
IA_FIRST_RUN_FIXATION_COUNT has 863 (43.1%) zerosZeros
IA_FIXATION_% has 863 (43.1%) zerosZeros
IA_LAST_FIXATION_DURATION has 863 (43.1%) zerosZeros
IA_LAST_RUN_DWELL_TIME has 863 (43.1%) zerosZeros
IA_LAST_RUN_FIXATION_COUNT has 863 (43.1%) zerosZeros
IA_REGRESSION_IN_COUNT has 1666 (83.3%) zerosZeros
IA_REGRESSION_OUT_COUNT has 1811 (90.5%) zerosZeros
IA_SELECTIVE_REGRESSION_PATH_DURATION has 863 (43.1%) zerosZeros
IA_SPILLOVER has 1737 (86.9%) zerosZeros
min_IA_ID has 2000 (100.0%) zerosZeros
part_min_IA_ID has 869 (43.5%) zerosZeros
subtlex_Frequency has 147 (7.3%) zerosZeros
Token_idx has 22 (1.1%) zerosZeros
n_Lefts has 1367 (68.3%) zerosZeros
n_Rights has 1291 (64.5%) zerosZeros
Distance2Head has 126 (6.3%) zerosZeros
prev_subtlex_Frequency has 135 (6.8%) zerosZeros
regression_rate has 808 (40.4%) zerosZeros
normalized_dwell_time has 863 (43.1%) zerosZeros
normalized_part_dwell_time has 858 (42.9%) zerosZeros
normalized_part_ID has 53 (2.6%) zerosZeros
reverse_ID has 21 (1.1%) zerosZeros
reverse_part_ID has 56 (2.8%) zerosZeros
normalized_ID has 22 (1.1%) zerosZeros
cs_has_two_questions has 674 (33.7%) zerosZeros
q_reference has 1030 (51.5%) zerosZeros

Reproduction

Analysis started2024-03-25 11:34:02.448353
Analysis finished2024-03-25 11:34:04.334436
Duration1.89 second
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIQUE 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1280428.191
Minimum202
Maximum2532226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:04.421651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum202
5-th percentile133634.3
Q1659192.5
median1309961.5
Q31908154.25
95-th percentile2401737.6
Maximum2532226
Range2532024
Interquartile range (IQR)1248961.75

Descriptive statistics

Standard deviation728001.375
Coefficient of variation (CV)0.5685608769
Kurtosis-1.192853765
Mean1280428.191
Median Absolute Deviation (MAD)632369.5
Skewness-0.0435276347
Sum2560856382
Variance5.29986002 × 1011
MonotonicityNot monotonic
2024-03-25T13:34:04.548434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2403830 1
 
0.1%
1749501 1
 
0.1%
2117455 1
 
0.1%
1422095 1
 
0.1%
1793406 1
 
0.1%
701039 1
 
0.1%
191180 1
 
0.1%
2103136 1
 
0.1%
809338 1
 
0.1%
1359168 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
202 1
0.1%
1058 1
0.1%
2135 1
0.1%
4150 1
0.1%
5059 1
0.1%
ValueCountFrequency (%)
2532226 1
0.1%
2529145 1
0.1%
2528890 1
0.1%
2528663 1
0.1%
2527844 1
0.1%

TRIAL_INDEX
Real number (ℝ)

Distinct96
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.292
Minimum3
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:04.673449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7
Q121
median39
Q357
95-th percentile75
Maximum109
Range106
Interquartile range (IQR)36

Descriptive statistics

Standard deviation22.00092626
Coefficient of variation (CV)0.5599339881
Kurtosis-0.8553138332
Mean39.292
Median Absolute Deviation (MAD)18
Skewness0.2133958535
Sum78584
Variance484.0407564
MonotonicityNot monotonic
2024-03-25T13:34:04.790727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 39
 
1.9%
27 36
 
1.8%
30 36
 
1.8%
9 36
 
1.8%
26 35
 
1.8%
42 35
 
1.8%
38 34
 
1.7%
5 33
 
1.7%
15 33
 
1.7%
13 33
 
1.7%
Other values (86) 1650
82.5%
ValueCountFrequency (%)
3 20
1.0%
4 17
0.9%
5 33
1.7%
6 19
0.9%
7 39
1.9%
ValueCountFrequency (%)
109 2
0.1%
102 1
0.1%
98 2
0.1%
96 1
0.1%
95 2
0.1%

list
Real number (ℝ)

Distinct60
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.3765
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:04.905941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q115
median30
Q346
95-th percentile58
Maximum60
Range59
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.75337542
Coefficient of variation (CV)0.5844444035
Kurtosis-1.244139945
Mean30.3765
Median Absolute Deviation (MAD)16
Skewness0.0229178475
Sum60753
Variance315.1823389
MonotonicityNot monotonic
2024-03-25T13:34:05.018059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 47
 
2.4%
8 46
 
2.3%
55 45
 
2.2%
2 44
 
2.2%
27 43
 
2.1%
7 43
 
2.1%
52 42
 
2.1%
30 41
 
2.1%
23 40
 
2.0%
9 38
 
1.9%
Other values (50) 1571
78.5%
ValueCountFrequency (%)
1 34
1.7%
2 44
2.2%
3 27
1.4%
4 32
1.6%
5 36
1.8%
ValueCountFrequency (%)
60 47
2.4%
59 32
1.6%
58 37
1.8%
57 30
1.5%
56 33
1.7%

article_ind
Real number (ℝ)

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4315
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:05.119663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.454167539
Coefficient of variation (CV)0.5370702851
Kurtosis-1.202289581
Mean6.4315
Median Absolute Deviation (MAD)3
Skewness0.03012934358
Sum12863
Variance11.93127339
MonotonicityNot monotonic
2024-03-25T13:34:05.209027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 183
9.2%
5 181
9.0%
1 178
8.9%
4 173
8.6%
10 168
8.4%
12 168
8.4%
3 164
8.2%
2 162
8.1%
8 159
8.0%
11 156
7.8%
Other values (2) 308
15.4%
ValueCountFrequency (%)
1 178
8.9%
2 162
8.1%
3 164
8.2%
4 173
8.6%
5 181
9.0%
ValueCountFrequency (%)
12 168
8.4%
11 156
7.8%
10 168
8.4%
9 153
7.6%
8 159
8.0%

reread
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.162
Minimum0
Maximum1
Zeros1676
Zeros (%)83.8%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:05.295859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3685429581
Coefficient of variation (CV)2.274956531
Kurtosis1.372584489
Mean0.162
Median Absolute Deviation (MAD)0
Skewness1.836086301
Sum324
Variance0.135823912
MonotonicityNot monotonic
2024-03-25T13:34:05.379830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 1676
83.8%
1 324
 
16.2%
ValueCountFrequency (%)
0 1676
83.8%
1 324
 
16.2%
ValueCountFrequency (%)
1 324
 
16.2%
0 1676
83.8%

practice
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros2000
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:05.462772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-03-25T13:34:05.535737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 2000
100.0%
ValueCountFrequency (%)
0 2000
100.0%
ValueCountFrequency (%)
0 2000
100.0%

q_ind
Real number (ℝ)

ZEROS 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9835
Minimum0
Maximum2
Zeros697
Zeros (%)34.8%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:05.614719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.8249654744
Coefficient of variation (CV)0.8388057696
Kurtosis-1.530091453
Mean0.9835
Median Absolute Deviation (MAD)1
Skewness0.03063800635
Sum1967
Variance0.680568034
MonotonicityNot monotonic
2024-03-25T13:34:05.698866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0 697
34.8%
2 664
33.2%
1 639
31.9%
ValueCountFrequency (%)
0 697
34.8%
1 639
31.9%
2 664
33.2%
ValueCountFrequency (%)
2 664
33.2%
1 639
31.9%
0 697
34.8%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:05.795457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length7
Mean length7.963
Min length7

Characters and Unicode

Total characters15926
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHunting
2nd rowHunting
3rd rowHunting
4th rowHunting
5th rowGathering
ValueCountFrequency (%)
hunting 1037
51.8%
gathering 963
48.1%
2024-03-25T13:34:06.003817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 3037
19.1%
i 2000
12.6%
t 2000
12.6%
g 2000
12.6%
H 1037
 
6.5%
u 1037
 
6.5%
G 963
 
6.0%
a 963
 
6.0%
h 963
 
6.0%
e 963
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13926
87.4%
Uppercase Letter 2000
 
12.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 3037
21.8%
i 2000
14.4%
t 2000
14.4%
g 2000
14.4%
u 1037
 
7.4%
a 963
 
6.9%
h 963
 
6.9%
e 963
 
6.9%
r 963
 
6.9%
Uppercase Letter
ValueCountFrequency (%)
H 1037
51.8%
G 963
48.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 15926
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 3037
19.1%
i 2000
12.6%
t 2000
12.6%
g 2000
12.6%
H 1037
 
6.5%
u 1037
 
6.5%
G 963
 
6.0%
a 963
 
6.0%
h 963
 
6.0%
e 963
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 3037
19.1%
i 2000
12.6%
t 2000
12.6%
g 2000
12.6%
H 1037
 
6.5%
u 1037
 
6.5%
G 963
 
6.0%
a 963
 
6.0%
h 963
 
6.0%
e 963
 
6.0%
Distinct24
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:06.100921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters40000
Distinct characters9
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row['3', '2', '0', '1']
2nd row['1', '0', '3', '2']
3rd row['1', '3', '2', '0']
4th row['0', '1', '3', '2']
5th row['2', '0', '1', '3']
ValueCountFrequency (%)
3 2000
25.0%
1 2000
25.0%
2 2000
25.0%
0 2000
25.0%
2024-03-25T13:34:06.282966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 16000
40.0%
6000
 
15.0%
, 6000
 
15.0%
[ 2000
 
5.0%
3 2000
 
5.0%
1 2000
 
5.0%
2 2000
 
5.0%
0 2000
 
5.0%
] 2000
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 22000
55.0%
Decimal Number 8000
 
20.0%
Space Separator 6000
 
15.0%
Open Punctuation 2000
 
5.0%
Close Punctuation 2000
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2000
25.0%
1 2000
25.0%
2 2000
25.0%
0 2000
25.0%
Other Punctuation
ValueCountFrequency (%)
' 16000
72.7%
, 6000
 
27.3%
Space Separator
ValueCountFrequency (%)
6000
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2000
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
' 16000
40.0%
6000
 
15.0%
, 6000
 
15.0%
[ 2000
 
5.0%
3 2000
 
5.0%
1 2000
 
5.0%
2 2000
 
5.0%
0 2000
 
5.0%
] 2000
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 16000
40.0%
6000
 
15.0%
, 6000
 
15.0%
[ 2000
 
5.0%
3 2000
 
5.0%
1 2000
 
5.0%
2 2000
 
5.0%
0 2000
 
5.0%
] 2000
 
5.0%

correct_answer
Real number (ℝ)

ZEROS 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.464
Minimum0
Maximum3
Zeros521
Zeros (%)26.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:06.382108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum3
Range3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.116390238
Coefficient of variation (CV)0.762561638
Kurtosis-1.354741514
Mean1.464
Median Absolute Deviation (MAD)1
Skewness0.04180712754
Sum2928
Variance1.246327164
MonotonicityNot monotonic
2024-03-25T13:34:06.458054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
0 521
26.1%
1 506
25.3%
2 497
24.9%
3 476
23.8%
ValueCountFrequency (%)
0 521
26.1%
1 506
25.3%
2 497
24.9%
3 476
23.8%
ValueCountFrequency (%)
3 476
23.8%
2 497
24.9%
1 506
25.3%
0 521
26.1%

FINAL_ANSWER
Real number (ℝ)

ZEROS 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.471
Minimum0
Maximum3
Zeros515
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:06.542835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum3
Range3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.117042028
Coefficient of variation (CV)0.7593759535
Kurtosis-1.356197048
Mean1.471
Median Absolute Deviation (MAD)1
Skewness0.03828112964
Sum2942
Variance1.247782891
MonotonicityNot monotonic
2024-03-25T13:34:06.620610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
0 515
25.8%
1 511
25.6%
2 491
24.6%
3 483
24.1%
ValueCountFrequency (%)
0 515
25.8%
1 511
25.6%
2 491
24.6%
3 483
24.1%
ValueCountFrequency (%)
3 483
24.1%
2 491
24.6%
1 511
25.6%
0 515
25.8%

IA_ID
Real number (ℝ)

ZEROS 

Distinct152
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.886
Minimum0
Maximum153
Zeros22
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:06.727500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q127
median55
Q384
95-th percentile125
Maximum153
Range153
Interquartile range (IQR)57

Descriptive statistics

Standard deviation36.83494822
Coefficient of variation (CV)0.6363360437
Kurtosis-0.6944202222
Mean57.886
Median Absolute Deviation (MAD)28
Skewness0.3883048174
Sum115772
Variance1356.813411
MonotonicityNot monotonic
2024-03-25T13:34:06.843837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 29
 
1.5%
34 26
 
1.3%
18 25
 
1.2%
44 25
 
1.2%
78 24
 
1.2%
33 24
 
1.2%
25 23
 
1.1%
58 23
 
1.1%
5 23
 
1.1%
27 23
 
1.1%
Other values (142) 1755
87.8%
ValueCountFrequency (%)
0 22
1.1%
1 20
1.0%
2 21
1.1%
3 18
0.9%
4 11
0.5%
ValueCountFrequency (%)
153 2
0.1%
152 1
 
0.1%
151 1
 
0.1%
149 1
 
0.1%
148 4
0.2%
Distinct997
Distinct (%)49.9%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:07.053595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length18
Median length15
Mean length4.7065
Min length1

Characters and Unicode

Total characters9413
Distinct characters77
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique754 ?
Unique (%)37.7%

Sample

1st rowOberg
2nd rowearly.
3rd rowto
4th rowin
5th rowthe
ValueCountFrequency (%)
the 132
 
6.6%
and 62
 
3.1%
to 59
 
2.9%
a 58
 
2.9%
of 46
 
2.3%
is 40
 
2.0%
in 36
 
1.8%
that 24
 
1.2%
are 21
 
1.1%
for 21
 
1.1%
Other values (904) 1501
75.0%
2024-03-25T13:34:07.375001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1103
 
11.7%
t 759
 
8.1%
a 753
 
8.0%
o 647
 
6.9%
n 605
 
6.4%
i 596
 
6.3%
r 547
 
5.8%
s 545
 
5.8%
h 443
 
4.7%
l 360
 
3.8%
Other values (67) 3055
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8586
91.2%
Uppercase Letter 278
 
3.0%
Other Punctuation 276
 
2.9%
Decimal Number 172
 
1.8%
Dash Punctuation 34
 
0.4%
Initial Punctuation 26
 
0.3%
Final Punctuation 25
 
0.3%
Currency Symbol 6
 
0.1%
Open Punctuation 5
 
0.1%
Close Punctuation 5
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1103
12.8%
t 759
 
8.8%
a 753
 
8.8%
o 647
 
7.5%
n 605
 
7.0%
i 596
 
6.9%
r 547
 
6.4%
s 545
 
6.3%
h 443
 
5.2%
l 360
 
4.2%
Other values (16) 2228
25.9%
Uppercase Letter
ValueCountFrequency (%)
T 32
 
11.5%
I 24
 
8.6%
A 22
 
7.9%
B 20
 
7.2%
S 16
 
5.8%
H 16
 
5.8%
F 13
 
4.7%
O 13
 
4.7%
P 12
 
4.3%
C 11
 
4.0%
Other values (15) 99
35.6%
Decimal Number
ValueCountFrequency (%)
0 49
28.5%
1 47
27.3%
2 19
 
11.0%
3 13
 
7.6%
5 11
 
6.4%
4 10
 
5.8%
8 9
 
5.2%
9 6
 
3.5%
7 5
 
2.9%
6 3
 
1.7%
Other Punctuation
ValueCountFrequency (%)
, 128
46.4%
. 106
38.4%
' 31
 
11.2%
% 4
 
1.4%
; 3
 
1.1%
? 2
 
0.7%
: 2
 
0.7%
Currency Symbol
ValueCountFrequency (%)
£ 3
50.0%
$ 2
33.3%
1
 
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 27
79.4%
7
 
20.6%
Initial Punctuation
ValueCountFrequency (%)
26
100.0%
Final Punctuation
ValueCountFrequency (%)
25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8864
94.2%
Common 549
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1103
12.4%
t 759
 
8.6%
a 753
 
8.5%
o 647
 
7.3%
n 605
 
6.8%
i 596
 
6.7%
r 547
 
6.2%
s 545
 
6.1%
h 443
 
5.0%
l 360
 
4.1%
Other values (41) 2506
28.3%
Common
ValueCountFrequency (%)
, 128
23.3%
. 106
19.3%
0 49
 
8.9%
1 47
 
8.6%
' 31
 
5.6%
- 27
 
4.9%
26
 
4.7%
25
 
4.6%
2 19
 
3.5%
3 13
 
2.4%
Other values (16) 78
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9351
99.3%
Punctuation 58
 
0.6%
None 3
 
< 0.1%
Currency Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1103
 
11.8%
t 759
 
8.1%
a 753
 
8.1%
o 647
 
6.9%
n 605
 
6.5%
i 596
 
6.4%
r 547
 
5.8%
s 545
 
5.8%
h 443
 
4.7%
l 360
 
3.8%
Other values (62) 2993
32.0%
Punctuation
ValueCountFrequency (%)
26
44.8%
25
43.1%
7
 
12.1%
None
ValueCountFrequency (%)
£ 3
100.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%

IA_DWELL_TIME
Real number (ℝ)

ZEROS 

Distinct503
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.386
Minimum0
Maximum2104
Zeros863
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:07.510360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median130
Q3243.25
95-th percentile609.05
Maximum2104
Range2104
Interquartile range (IQR)243.25

Descriptive statistics

Standard deviation226.4716046
Coefficient of variation (CV)1.329167916
Kurtosis8.99755865
Mean170.386
Median Absolute Deviation (MAD)130
Skewness2.346453167
Sum340772
Variance51289.3877
MonotonicityNot monotonic
2024-03-25T13:34:07.621279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 863
43.1%
184 14
 
0.7%
161 11
 
0.5%
211 10
 
0.5%
170 10
 
0.5%
189 9
 
0.4%
188 9
 
0.4%
154 9
 
0.4%
159 9
 
0.4%
162 8
 
0.4%
Other values (493) 1048
52.4%
ValueCountFrequency (%)
0 863
43.1%
27 1
 
0.1%
34 1
 
0.1%
36 1
 
0.1%
37 1
 
0.1%
ValueCountFrequency (%)
2104 1
0.1%
1874 1
0.1%
1632 1
0.1%
1599 1
0.1%
1393 1
0.1%

IA_FIRST_FIXATION_DURATION
Real number (ℝ)

ZEROS 

Distinct309
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.686
Minimum0
Maximum724
Zeros863
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:07.732672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median113
Q3187
95-th percentile291
Maximum724
Range724
Interquartile range (IQR)187

Descriptive statistics

Standard deviation111.7555236
Coefficient of variation (CV)1.037790647
Kurtosis1.056666128
Mean107.686
Median Absolute Deviation (MAD)113
Skewness0.8443858072
Sum215372
Variance12489.29705
MonotonicityNot monotonic
2024-03-25T13:34:07.827039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 863
43.1%
184 16
 
0.8%
158 15
 
0.8%
161 14
 
0.7%
170 14
 
0.7%
189 14
 
0.7%
159 13
 
0.7%
211 12
 
0.6%
190 12
 
0.6%
162 12
 
0.6%
Other values (299) 1015
50.7%
ValueCountFrequency (%)
0 863
43.1%
5 1
 
0.1%
23 1
 
0.1%
26 1
 
0.1%
27 1
 
0.1%
ValueCountFrequency (%)
724 1
0.1%
709 1
0.1%
702 1
0.1%
687 1
0.1%
584 1
0.1%

IA_FIRST_RUN_DWELL_TIME
Real number (ℝ)

ZEROS 

Distinct354
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.579
Minimum0
Maximum1225
Zeros863
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:07.927015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median116.5
Q3193
95-th percentile353
Maximum1225
Range1225
Interquartile range (IQR)193

Descriptive statistics

Standard deviation136.7344467
Coefficient of variation (CV)1.14346538
Kurtosis7.287022583
Mean119.579
Median Absolute Deviation (MAD)116.5
Skewness1.7945278
Sum239158
Variance18696.30891
MonotonicityNot monotonic
2024-03-25T13:34:08.024904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 863
43.1%
184 16
 
0.8%
189 13
 
0.7%
161 13
 
0.7%
159 13
 
0.7%
170 13
 
0.7%
158 13
 
0.7%
211 12
 
0.6%
190 11
 
0.5%
162 11
 
0.5%
Other values (344) 1022
51.1%
ValueCountFrequency (%)
0 863
43.1%
23 1
 
0.1%
26 1
 
0.1%
27 1
 
0.1%
33 1
 
0.1%
ValueCountFrequency (%)
1225 1
0.1%
1205 1
0.1%
1123 1
0.1%
881 1
0.1%
879 1
0.1%

IA_REGRESSION_PATH_DURATION
Real number (ℝ)

ZEROS 

Distinct499
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.5245
Minimum0
Maximum9799
Zeros863
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:08.122436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median136
Q3240
95-th percentile771.15
Maximum9799
Range9799
Interquartile range (IQR)240

Descriptive statistics

Standard deviation633.9678549
Coefficient of variation (CV)2.680347511
Kurtosis100.982197
Mean236.5245
Median Absolute Deviation (MAD)136
Skewness8.861408985
Sum473049
Variance401915.241
MonotonicityNot monotonic
2024-03-25T13:34:08.221296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 863
43.1%
184 14
 
0.7%
189 12
 
0.6%
211 12
 
0.6%
158 11
 
0.5%
154 11
 
0.5%
159 11
 
0.5%
161 11
 
0.5%
170 9
 
0.4%
200 9
 
0.4%
Other values (489) 1037
51.8%
ValueCountFrequency (%)
0 863
43.1%
26 1
 
0.1%
27 1
 
0.1%
34 1
 
0.1%
37 1
 
0.1%
ValueCountFrequency (%)
9799 1
0.1%
9688 1
0.1%
8424 1
0.1%
7497 1
0.1%
7031 1
0.1%

IA_FIXATION_COUNT
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.894
Minimum0
Maximum8
Zeros863
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:08.302215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.079048735
Coefficient of variation (CV)1.206989637
Kurtosis5.610776372
Mean0.894
Median Absolute Deviation (MAD)1
Skewness1.873466427
Sum1788
Variance1.164346173
MonotonicityNot monotonic
2024-03-25T13:34:08.372557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 863
43.1%
1 738
36.9%
2 245
 
12.2%
3 95
 
4.8%
4 38
 
1.9%
5 11
 
0.5%
6 5
 
0.2%
8 3
 
0.1%
7 2
 
0.1%
ValueCountFrequency (%)
0 863
43.1%
1 738
36.9%
2 245
 
12.2%
3 95
 
4.8%
4 38
 
1.9%
ValueCountFrequency (%)
8 3
 
0.1%
7 2
 
0.1%
6 5
 
0.2%
5 11
 
0.5%
4 38
1.9%

IA_SKIP
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6165
Minimum0
Maximum1
Zeros767
Zeros (%)38.4%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:08.446019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4863599727
Coefficient of variation (CV)0.7889050652
Kurtosis-1.771805969
Mean0.6165
Median Absolute Deviation (MAD)0
Skewness-0.4795485779
Sum1233
Variance0.236546023
MonotonicityNot monotonic
2024-03-25T13:34:08.514928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 1233
61.7%
0 767
38.4%
ValueCountFrequency (%)
0 767
38.4%
1 1233
61.7%
ValueCountFrequency (%)
1 1233
61.7%
0 767
38.4%

IA_TOP
Real number (ℝ)

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean512.201
Minimum153
Maximum1176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:08.588591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum153
5-th percentile153
Q1264
median492
Q3720
95-th percentile948
Maximum1176
Range1023
Interquartile range (IQR)456

Descriptive statistics

Standard deviation262.6210226
Coefficient of variation (CV)0.512730398
Kurtosis-0.6203972845
Mean512.201
Median Absolute Deviation (MAD)228
Skewness0.4496331667
Sum1024402
Variance68969.8015
MonotonicityNot monotonic
2024-03-25T13:34:08.668633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
264 301
15.0%
606 294
14.7%
378 282
14.1%
492 281
14.1%
153 228
11.4%
720 199
10.0%
834 161
8.1%
948 93
 
4.7%
1062 74
 
3.7%
154 46
 
2.3%
Other values (9) 41
 
2.1%
ValueCountFrequency (%)
153 228
11.4%
154 46
 
2.3%
261 1
 
0.1%
263 3
 
0.1%
264 301
15.0%
ValueCountFrequency (%)
1176 23
 
1.1%
1062 74
3.7%
1061 1
 
0.1%
948 93
4.7%
834 161
8.1%

IA_LEFT
Real number (ℝ)

Distinct93
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1150.984
Minimum358
Maximum2125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:08.758799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum358
5-th percentile358
Q1700
median1137
Q31574
95-th percentile1973
Maximum2125
Range1767
Interquartile range (IQR)874

Descriptive statistics

Standard deviation505.7457989
Coefficient of variation (CV)0.4394029795
Kurtosis-1.171572937
Mean1150.984
Median Absolute Deviation (MAD)437
Skewness0.06854680559
Sum2301968
Variance255778.8132
MonotonicityNot monotonic
2024-03-25T13:34:08.855541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
358 141
 
7.0%
510 33
 
1.7%
491 33
 
1.7%
1745 30
 
1.5%
833 30
 
1.5%
1194 29
 
1.5%
1061 29
 
1.5%
624 29
 
1.5%
1992 28
 
1.4%
586 28
 
1.4%
Other values (83) 1590
79.5%
ValueCountFrequency (%)
358 141
7.0%
396 2
 
0.1%
415 11
 
0.5%
434 27
 
1.4%
453 11
 
0.5%
ValueCountFrequency (%)
2125 3
 
0.1%
2106 6
0.3%
2087 3
 
0.1%
2068 11
0.5%
2049 5
0.2%

IA_AVERAGE_FIX_PUPIL_SIZE
Real number (ℝ)

MISSING 

Distinct797
Distinct (%)70.1%
Missing863
Missing (%)43.1%
Infinite0
Infinite (%)0.0%
Mean862.8163412
Minimum320.5
Maximum1732
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:08.954702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum320.5
5-th percentile493.8
Q1677
median840
Q31011
95-th percentile1321.6
Maximum1732
Range1411.5
Interquartile range (IQR)334

Descriptive statistics

Standard deviation248.9586274
Coefficient of variation (CV)0.2885418548
Kurtosis0.1297035597
Mean862.8163412
Median Absolute Deviation (MAD)166.5
Skewness0.5105721865
Sum981022.18
Variance61980.39818
MonotonicityNot monotonic
2024-03-25T13:34:09.046790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
768 7
 
0.4%
853 5
 
0.2%
1191 5
 
0.2%
708 5
 
0.2%
731 4
 
0.2%
789 4
 
0.2%
577 4
 
0.2%
664 4
 
0.2%
623 4
 
0.2%
994 4
 
0.2%
Other values (787) 1091
54.5%
(Missing) 863
43.1%
ValueCountFrequency (%)
320.5 1
0.1%
323 1
0.1%
346 1
0.1%
360.5 1
0.1%
363 1
0.1%
ValueCountFrequency (%)
1732 1
0.1%
1692 1
0.1%
1691.8 1
0.1%
1669 1
0.1%
1652 1
0.1%

IA_DWELL_TIME_%
Real number (ℝ)

ZEROS 

Distinct346
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00902755
Minimum0
Maximum0.1404
Zeros863
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:09.148371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0058
Q30.0142
95-th percentile0.031815
Maximum0.1404
Range0.1404
Interquartile range (IQR)0.0142

Descriptive statistics

Standard deviation0.01199989645
Coefficient of variation (CV)1.329252837
Kurtosis13.45166787
Mean0.00902755
Median Absolute Deviation (MAD)0.0058
Skewness2.555529656
Sum18.0551
Variance0.0001439975148
MonotonicityNot monotonic
2024-03-25T13:34:09.539116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 863
43.1%
0.0085 13
 
0.7%
0.0102 12
 
0.6%
0.0119 11
 
0.5%
0.0097 10
 
0.5%
0.0055 10
 
0.5%
0.0086 10
 
0.5%
0.0087 10
 
0.5%
0.0047 10
 
0.5%
0.0075 10
 
0.5%
Other values (336) 1041
52.0%
ValueCountFrequency (%)
0 863
43.1%
0.001 1
 
0.1%
0.0013 1
 
0.1%
0.0015 1
 
0.1%
0.0017 1
 
0.1%
ValueCountFrequency (%)
0.1404 1
0.1%
0.1091 1
0.1%
0.0968 1
0.1%
0.078 1
0.1%
0.0779 1
0.1%

IA_RUN_COUNT
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.802
Minimum0
Maximum7
Zeros863
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:09.622582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9062045041
Coefficient of variation (CV)1.129930803
Kurtosis3.281744388
Mean0.802
Median Absolute Deviation (MAD)1
Skewness1.470012567
Sum1604
Variance0.8212066033
MonotonicityNot monotonic
2024-03-25T13:34:09.687260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 863
43.1%
1 812
40.6%
2 223
 
11.2%
3 72
 
3.6%
4 22
 
1.1%
5 7
 
0.4%
7 1
 
0.1%
ValueCountFrequency (%)
0 863
43.1%
1 812
40.6%
2 223
 
11.2%
3 72
 
3.6%
4 22
 
1.1%
ValueCountFrequency (%)
7 1
 
0.1%
5 7
 
0.4%
4 22
 
1.1%
3 72
 
3.6%
2 223
11.2%

IA_REGRESSION_OUT_FULL_COUNT
Real number (ℝ)

ZEROS 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.193
Minimum0
Maximum4
Zeros1671
Zeros (%)83.5%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:09.761253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4720830766
Coefficient of variation (CV)2.446026304
Kurtosis9.043445295
Mean0.193
Median Absolute Deviation (MAD)0
Skewness2.768520458
Sum386
Variance0.2228624312
MonotonicityNot monotonic
2024-03-25T13:34:09.832156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 1671
83.5%
1 281
 
14.1%
2 40
 
2.0%
3 7
 
0.4%
4 1
 
0.1%
ValueCountFrequency (%)
0 1671
83.5%
1 281
 
14.1%
2 40
 
2.0%
3 7
 
0.4%
4 1
 
0.1%
ValueCountFrequency (%)
4 1
 
0.1%
3 7
 
0.4%
2 40
 
2.0%
1 281
 
14.1%
0 1671
83.5%

IA_FIRST_FIX_PROGRESSIVE
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3835
Minimum0
Maximum1
Zeros1233
Zeros (%)61.7%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:09.910374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4863599727
Coefficient of variation (CV)1.268213749
Kurtosis-1.771805969
Mean0.3835
Median Absolute Deviation (MAD)0
Skewness0.4795485779
Sum767
Variance0.236546023
MonotonicityNot monotonic
2024-03-25T13:34:09.977623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 1233
61.7%
1 767
38.4%
ValueCountFrequency (%)
0 1233
61.7%
1 767
38.4%
ValueCountFrequency (%)
1 767
38.4%
0 1233
61.7%

PARAGRAPH_RT
Real number (ℝ)

Distinct1869
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23745.3245
Minimum2014
Maximum117514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:10.063678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile7782.2
Q114803.5
median21599.5
Q329717.5
95-th percentile46454.8
Maximum117514
Range115500
Interquartile range (IQR)14914

Descriptive statistics

Standard deviation12630.40846
Coefficient of variation (CV)0.5319113857
Kurtosis3.607212167
Mean23745.3245
Median Absolute Deviation (MAD)7416.5
Skewness1.340489469
Sum47490649
Variance159527217.8
MonotonicityNot monotonic
2024-03-25T13:34:10.162696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16035 3
 
0.1%
27466 3
 
0.1%
25200 3
 
0.1%
22543 3
 
0.1%
31218 3
 
0.1%
18829 3
 
0.1%
10060 3
 
0.1%
26208 3
 
0.1%
27745 2
 
0.1%
16499 2
 
0.1%
Other values (1859) 1972
98.6%
ValueCountFrequency (%)
2014 1
0.1%
2063 1
0.1%
2340 1
0.1%
2402 1
0.1%
2555 1
0.1%
ValueCountFrequency (%)
117514 1
0.1%
90878 1
0.1%
88314 1
0.1%
83592 2
0.1%
77842 1
0.1%

IA_FIRST_FIXATION_VISITED_IA_COUNT
Real number (ℝ)

ZEROS 

Distinct106
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.898
Minimum0
Maximum117
Zeros882
Zeros (%)44.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:10.258834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q335
95-th percentile72
Maximum117
Range117
Interquartile range (IQR)35

Descriptive statistics

Standard deviation25.13930366
Coefficient of variation (CV)1.263408566
Kurtosis0.5005514134
Mean19.898
Median Absolute Deviation (MAD)7
Skewness1.175011451
Sum39796
Variance631.9845883
MonotonicityNot monotonic
2024-03-25T13:34:10.357508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 882
44.1%
25 28
 
1.4%
9 26
 
1.3%
8 25
 
1.2%
34 24
 
1.2%
27 23
 
1.1%
19 21
 
1.1%
1 21
 
1.1%
23 21
 
1.1%
28 21
 
1.1%
Other values (96) 908
45.4%
ValueCountFrequency (%)
0 882
44.1%
1 21
 
1.1%
2 20
 
1.0%
3 14
 
0.7%
4 10
 
0.5%
ValueCountFrequency (%)
117 1
0.1%
113 1
0.1%
109 1
0.1%
107 1
0.1%
105 1
0.1%

IA_FIRST_RUN_FIXATION_%
Real number (ℝ)

MISSING 

Distinct182
Distinct (%)16.0%
Missing863
Missing (%)43.1%
Infinite0
Infinite (%)0.0%
Mean0.01206754617
Minimum0.0025
Maximum0.1111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:10.460890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0025
5-th percentile0.0048
Q10.0073
median0.0101
Q30.0143
95-th percentile0.0256
Maximum0.1111
Range0.1086
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.007764005544
Coefficient of variation (CV)0.6433789796
Kurtosis30.79157795
Mean0.01206754617
Median Absolute Deviation (MAD)0.0032
Skewness3.777891022
Sum13.7208
Variance6.027978209 × 10-5
MonotonicityNot monotonic
2024-03-25T13:34:10.562580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0078 20
 
1.0%
0.0057 16
 
0.8%
0.0074 15
 
0.8%
0.007 15
 
0.8%
0.0085 15
 
0.8%
0.0072 15
 
0.8%
0.0093 15
 
0.8%
0.0075 14
 
0.7%
0.0137 14
 
0.7%
0.0112 14
 
0.7%
Other values (172) 984
49.2%
(Missing) 863
43.1%
ValueCountFrequency (%)
0.0025 1
0.1%
0.0027 1
0.1%
0.0028 2
0.1%
0.003 1
0.1%
0.0031 2
0.1%
ValueCountFrequency (%)
0.1111 1
0.1%
0.0769 1
0.1%
0.0625 1
0.1%
0.0526 1
0.1%
0.0476 1
0.1%

IA_FIRST_RUN_FIXATION_COUNT
Real number (ℝ)

ZEROS 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6315
Minimum0
Maximum4
Zeros863
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:10.645576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6155462603
Coefficient of variation (CV)0.9747367543
Kurtosis1.422484546
Mean0.6315
Median Absolute Deviation (MAD)0
Skewness0.7394434092
Sum1263
Variance0.3788971986
MonotonicityNot monotonic
2024-03-25T13:34:10.714006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
1 1027
51.3%
0 863
43.1%
2 98
 
4.9%
3 8
 
0.4%
4 4
 
0.2%
ValueCountFrequency (%)
0 863
43.1%
1 1027
51.3%
2 98
 
4.9%
3 8
 
0.4%
4 4
 
0.2%
ValueCountFrequency (%)
4 4
 
0.2%
3 8
 
0.4%
2 98
 
4.9%
1 1027
51.3%
0 863
43.1%

IA_FIRST_SACCADE_AMPLITUDE
Real number (ℝ)

MISSING 

Distinct625
Distinct (%)55.9%
Missing882
Missing (%)44.1%
Infinite0
Infinite (%)0.0%
Mean5.636171735
Minimum0.33
Maximum27.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:10.807370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile1.2885
Q12.26
median3.375
Q35.415
95-th percentile24.253
Maximum27.49
Range27.16
Interquartile range (IQR)3.155

Descriptive statistics

Standard deviation6.293811914
Coefficient of variation (CV)1.116682069
Kurtosis4.013289968
Mean5.636171735
Median Absolute Deviation (MAD)1.335
Skewness2.262276944
Sum6301.24
Variance39.61206841
MonotonicityNot monotonic
2024-03-25T13:34:10.906928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.27 9
 
0.4%
1.93 9
 
0.4%
2.77 8
 
0.4%
1.91 7
 
0.4%
2.44 7
 
0.4%
2.66 6
 
0.3%
2.05 6
 
0.3%
3.49 6
 
0.3%
2.25 6
 
0.3%
2.51 6
 
0.3%
Other values (615) 1048
52.4%
(Missing) 882
44.1%
ValueCountFrequency (%)
0.33 1
0.1%
0.44 1
0.1%
0.47 2
0.1%
0.53 1
0.1%
0.59 1
0.1%
ValueCountFrequency (%)
27.49 1
0.1%
27.4 1
0.1%
27.36 1
0.1%
27.16 1
0.1%
26.9 1
0.1%

IA_FIRST_SACCADE_ANGLE
Real number (ℝ)

MISSING 

Distinct917
Distinct (%)82.0%
Missing882
Missing (%)44.1%
Infinite0
Infinite (%)0.0%
Mean-22.25611807
Minimum-179.93
Maximum179.97
Zeros4
Zeros (%)0.2%
Negative628
Negative (%)31.4%
Memory size31.2 KiB
2024-03-25T13:34:11.002908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-179.93
5-th percentile-176.9805
Q1-6.605
median-0.76
Q32.81
95-th percentile172.3565
Maximum179.97
Range359.9
Interquartile range (IQR)9.415

Descriptive statistics

Standard deviation88.6722272
Coefficient of variation (CV)-3.984173113
Kurtosis0.5014493557
Mean-22.25611807
Median Absolute Deviation (MAD)4.34
Skewness-0.1990004852
Sum-24882.34
Variance7862.763876
MonotonicityNot monotonic
2024-03-25T13:34:11.095482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
0.2%
0.87 4
 
0.2%
0.98 4
 
0.2%
1.09 4
 
0.2%
-3.45 4
 
0.2%
-0.87 4
 
0.2%
1.33 4
 
0.2%
-0.47 4
 
0.2%
2.39 3
 
0.1%
2.03 3
 
0.1%
Other values (907) 1080
54.0%
(Missing) 882
44.1%
ValueCountFrequency (%)
-179.93 1
0.1%
-179.92 1
0.1%
-179.73 1
0.1%
-179.67 1
0.1%
-179.6 1
0.1%
ValueCountFrequency (%)
179.97 1
0.1%
179.95 1
0.1%
179.84 1
0.1%
179.81 1
0.1%
179.79 1
0.1%

IA_FIXATION_%
Real number (ℝ)

ZEROS 

Distinct269
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0091174
Minimum0
Maximum0.1111
Zeros863
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:11.195512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0069
Q30.0141
95-th percentile0.0303
Maximum0.1111
Range0.1111
Interquartile range (IQR)0.0141

Descriptive statistics

Standard deviation0.01152198574
Coefficient of variation (CV)1.263735905
Kurtosis8.252515773
Mean0.0091174
Median Absolute Deviation (MAD)0.0069
Skewness2.138901054
Sum18.2348
Variance0.0001327561553
MonotonicityNot monotonic
2024-03-25T13:34:11.290267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 863
43.1%
0.0133 13
 
0.7%
0.0132 13
 
0.7%
0.0114 13
 
0.7%
0.0244 13
 
0.7%
0.0119 13
 
0.7%
0.0085 13
 
0.7%
0.007 12
 
0.6%
0.0078 12
 
0.6%
0.0093 12
 
0.6%
Other values (259) 1023
51.1%
ValueCountFrequency (%)
0 863
43.1%
0.0025 1
 
0.1%
0.003 1
 
0.1%
0.0032 1
 
0.1%
0.0034 1
 
0.1%
ValueCountFrequency (%)
0.1111 1
0.1%
0.087 1
0.1%
0.0833 2
0.1%
0.0769 1
0.1%
0.0708 1
0.1%

IA_LAST_FIXATION_DURATION
Real number (ℝ)

ZEROS 

Distinct317
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.291
Minimum0
Maximum724
Zeros863
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:11.386396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median111
Q3185
95-th percentile293.05
Maximum724
Range724
Interquartile range (IQR)185

Descriptive statistics

Standard deviation111.8009662
Coefficient of variation (CV)1.042034898
Kurtosis0.8076428076
Mean107.291
Median Absolute Deviation (MAD)111
Skewness0.8313431518
Sum214582
Variance12499.45605
MonotonicityNot monotonic
2024-03-25T13:34:11.479944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 863
43.1%
170 15
 
0.8%
184 13
 
0.7%
188 12
 
0.6%
163 12
 
0.6%
157 11
 
0.5%
159 11
 
0.5%
189 11
 
0.5%
152 11
 
0.5%
154 11
 
0.5%
Other values (307) 1030
51.5%
ValueCountFrequency (%)
0 863
43.1%
15 1
 
0.1%
19 1
 
0.1%
27 2
 
0.1%
30 1
 
0.1%
ValueCountFrequency (%)
724 1
0.1%
709 1
0.1%
650 1
0.1%
584 1
0.1%
578 1
0.1%

IA_LAST_RUN_DWELL_TIME
Real number (ℝ)

ZEROS 

Distinct359
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.199
Minimum0
Maximum1123
Zeros863
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:11.582241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median116
Q3194
95-th percentile351.1
Maximum1123
Range1123
Interquartile range (IQR)194

Descriptive statistics

Standard deviation133.7217078
Coefficient of variation (CV)1.121835819
Kurtosis4.252719162
Mean119.199
Median Absolute Deviation (MAD)116
Skewness1.465942269
Sum238398
Variance17881.49515
MonotonicityNot monotonic
2024-03-25T13:34:11.673736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 863
43.1%
170 14
 
0.7%
184 13
 
0.7%
157 11
 
0.5%
188 11
 
0.5%
154 11
 
0.5%
189 11
 
0.5%
161 11
 
0.5%
163 10
 
0.5%
211 10
 
0.5%
Other values (349) 1035
51.7%
ValueCountFrequency (%)
0 863
43.1%
15 1
 
0.1%
19 1
 
0.1%
27 2
 
0.1%
30 1
 
0.1%
ValueCountFrequency (%)
1123 1
0.1%
883 1
0.1%
881 1
0.1%
879 1
0.1%
801 1
0.1%

IA_LAST_RUN_FIXATION_%
Real number (ℝ)

MISSING 

Distinct179
Distinct (%)15.7%
Missing863
Missing (%)43.1%
Infinite0
Infinite (%)0.0%
Mean0.01196411609
Minimum0.0025
Maximum0.1111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:11.771629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0025
5-th percentile0.00488
Q10.0074
median0.0101
Q30.0141
95-th percentile0.02536
Maximum0.1111
Range0.1086
Interquartile range (IQR)0.0067

Descriptive statistics

Standard deviation0.007659261857
Coefficient of variation (CV)0.6401861865
Kurtosis32.7669808
Mean0.01196411609
Median Absolute Deviation (MAD)0.0031
Skewness3.928497978
Sum13.6032
Variance5.86642922 × 10-5
MonotonicityNot monotonic
2024-03-25T13:34:11.871957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0078 19
 
0.9%
0.0081 18
 
0.9%
0.0057 16
 
0.8%
0.0085 16
 
0.8%
0.0074 15
 
0.8%
0.0072 15
 
0.8%
0.007 15
 
0.8%
0.0083 14
 
0.7%
0.0071 14
 
0.7%
0.0093 14
 
0.7%
Other values (169) 981
49.0%
(Missing) 863
43.1%
ValueCountFrequency (%)
0.0025 1
0.1%
0.0027 1
0.1%
0.0028 2
0.1%
0.003 1
0.1%
0.0031 1
0.1%
ValueCountFrequency (%)
0.1111 1
0.1%
0.0769 1
0.1%
0.0625 1
0.1%
0.0526 1
0.1%
0.0476 1
0.1%

IA_LAST_RUN_FIXATION_COUNT
Real number (ℝ)

ZEROS 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6265
Minimum0
Maximum4
Zeros863
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:11.954122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6026436258
Coefficient of variation (CV)0.9619211904
Kurtosis0.8788363986
Mean0.6265
Median Absolute Deviation (MAD)0
Skewness0.6101553992
Sum1253
Variance0.3631793397
MonotonicityNot monotonic
2024-03-25T13:34:12.024023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
1 1031
51.5%
0 863
43.1%
2 99
 
5.0%
3 4
 
0.2%
4 3
 
0.1%
ValueCountFrequency (%)
0 863
43.1%
1 1031
51.5%
2 99
 
5.0%
3 4
 
0.2%
4 3
 
0.1%
ValueCountFrequency (%)
4 3
 
0.1%
3 4
 
0.2%
2 99
 
5.0%
1 1031
51.5%
0 863
43.1%

IA_LAST_SACCADE_AMPLITUDE
Real number (ℝ)

MISSING 

Distinct631
Distinct (%)56.2%
Missing878
Missing (%)43.9%
Infinite0
Infinite (%)0.0%
Mean5.111167558
Minimum0.1
Maximum27.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:12.115664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.85
Q12
median3.125
Q35.1825
95-th percentile23.012
Maximum27.73
Range27.63
Interquartile range (IQR)3.1825

Descriptive statistics

Standard deviation5.827114248
Coefficient of variation (CV)1.140074979
Kurtosis5.379002053
Mean5.111167558
Median Absolute Deviation (MAD)1.36
Skewness2.453628012
Sum5734.73
Variance33.95526046
MonotonicityNot monotonic
2024-03-25T13:34:12.206970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.77 7
 
0.4%
3.27 6
 
0.3%
3.65 6
 
0.3%
2.24 6
 
0.3%
1.75 6
 
0.3%
1.55 6
 
0.3%
1.64 6
 
0.3%
1.93 6
 
0.3%
1.3 6
 
0.3%
1.91 6
 
0.3%
Other values (621) 1061
53.0%
(Missing) 878
43.9%
ValueCountFrequency (%)
0.1 1
0.1%
0.12 1
0.1%
0.23 1
0.1%
0.24 1
0.1%
0.25 1
0.1%
ValueCountFrequency (%)
27.73 1
0.1%
27.49 1
0.1%
27.4 1
0.1%
27.36 1
0.1%
27.16 1
0.1%

IA_LAST_SACCADE_ANGLE
Real number (ℝ)

MISSING 

Distinct960
Distinct (%)85.6%
Missing878
Missing (%)43.9%
Infinite0
Infinite (%)0.0%
Mean-12.78660428
Minimum-179.93
Maximum179.97
Zeros5
Zeros (%)0.2%
Negative579
Negative (%)28.9%
Memory size31.2 KiB
2024-03-25T13:34:12.303397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-179.93
5-th percentile-177.495
Q1-6.38
median-0.245
Q34.72
95-th percentile176.83
Maximum179.97
Range359.9
Interquartile range (IQR)11.1

Descriptive statistics

Standard deviation97.69546182
Coefficient of variation (CV)-7.640454002
Kurtosis0.05949800878
Mean-12.78660428
Median Absolute Deviation (MAD)5.26
Skewness-0.05363289422
Sum-14346.57
Variance9544.403261
MonotonicityNot monotonic
2024-03-25T13:34:12.394213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.87 6
 
0.3%
0 5
 
0.2%
-0.67 4
 
0.2%
1.33 4
 
0.2%
-0.85 3
 
0.1%
-1.36 3
 
0.1%
-0.47 3
 
0.1%
0.98 3
 
0.1%
-0.04 3
 
0.1%
-1.35 3
 
0.1%
Other values (950) 1085
54.2%
(Missing) 878
43.9%
ValueCountFrequency (%)
-179.93 1
0.1%
-179.71 1
0.1%
-179.67 1
0.1%
-179.64 1
0.1%
-179.6 1
0.1%
ValueCountFrequency (%)
179.97 1
0.1%
179.91 1
0.1%
179.84 1
0.1%
179.81 1
0.1%
179.79 1
0.1%

IA_REGRESSION_IN_COUNT
Real number (ℝ)

ZEROS 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1945
Minimum0
Maximum4
Zeros1666
Zeros (%)83.3%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:12.478017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4698724721
Coefficient of variation (CV)2.415796772
Kurtosis8.624147994
Mean0.1945
Median Absolute Deviation (MAD)0
Skewness2.70682777
Sum389
Variance0.2207801401
MonotonicityNot monotonic
2024-03-25T13:34:12.547275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 1666
83.3%
1 287
 
14.3%
2 40
 
2.0%
3 6
 
0.3%
4 1
 
0.1%
ValueCountFrequency (%)
0 1666
83.3%
1 287
 
14.3%
2 40
 
2.0%
3 6
 
0.3%
4 1
 
0.1%
ValueCountFrequency (%)
4 1
 
0.1%
3 6
 
0.3%
2 40
 
2.0%
1 287
 
14.3%
0 1666
83.3%

IA_REGRESSION_OUT_COUNT
Real number (ℝ)

ZEROS 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.102
Minimum0
Maximum3
Zeros1811
Zeros (%)90.5%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:12.623334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3296214877
Coefficient of variation (CV)3.231583213
Kurtosis13.93173932
Mean0.102
Median Absolute Deviation (MAD)0
Skewness3.487393315
Sum204
Variance0.1086503252
MonotonicityNot monotonic
2024-03-25T13:34:12.687735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
0 1811
90.5%
1 176
 
8.8%
2 11
 
0.5%
3 2
 
0.1%
ValueCountFrequency (%)
0 1811
90.5%
1 176
 
8.8%
2 11
 
0.5%
3 2
 
0.1%
ValueCountFrequency (%)
3 2
 
0.1%
2 11
 
0.5%
1 176
 
8.8%
0 1811
90.5%

IA_SELECTIVE_REGRESSION_PATH_DURATION
Real number (ℝ)

ZEROS 

Distinct392
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.348
Minimum0
Maximum1542
Zeros863
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:12.778995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median121
Q3202
95-th percentile407.05
Maximum1542
Range1542
Interquartile range (IQR)202

Descriptive statistics

Standard deviation159.7068722
Coefficient of variation (CV)1.215906388
Kurtosis8.951421248
Mean131.348
Median Absolute Deviation (MAD)121
Skewness2.161283088
Sum262696
Variance25506.28504
MonotonicityNot monotonic
2024-03-25T13:34:12.873151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 863
43.1%
184 15
 
0.8%
211 13
 
0.7%
170 13
 
0.7%
161 13
 
0.7%
189 13
 
0.7%
159 12
 
0.6%
154 11
 
0.5%
162 11
 
0.5%
158 11
 
0.5%
Other values (382) 1025
51.2%
ValueCountFrequency (%)
0 863
43.1%
26 1
 
0.1%
27 1
 
0.1%
34 1
 
0.1%
36 1
 
0.1%
ValueCountFrequency (%)
1542 1
0.1%
1225 1
0.1%
1169 1
0.1%
1132 1
0.1%
1123 1
0.1%

IA_SPILLOVER
Real number (ℝ)

ZEROS 

Distinct162
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.656
Minimum0
Maximum784
Zeros1737
Zeros (%)86.9%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:12.976973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile196
Maximum784
Range784
Interquartile range (IQR)0

Descriptive statistics

Standard deviation72.77157766
Coefficient of variation (CV)2.836435051
Kurtosis14.37048318
Mean25.656
Median Absolute Deviation (MAD)0
Skewness3.347908266
Sum51312
Variance5295.702515
MonotonicityNot monotonic
2024-03-25T13:34:13.075577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1737
86.9%
165 5
 
0.2%
172 5
 
0.2%
155 4
 
0.2%
168 4
 
0.2%
180 4
 
0.2%
183 4
 
0.2%
262 4
 
0.2%
133 4
 
0.2%
226 4
 
0.2%
Other values (152) 225
 
11.2%
ValueCountFrequency (%)
0 1737
86.9%
39 1
 
0.1%
68 1
 
0.1%
69 1
 
0.1%
70 1
 
0.1%
ValueCountFrequency (%)
784 1
0.1%
549 1
0.1%
536 1
0.1%
526 1
0.1%
421 1
0.1%

TRIAL_DWELL_TIME
Real number (ℝ)

Distinct1860
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19230.9875
Minimum1650
Maximum101794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:13.180777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1650
5-th percentile6083.15
Q111796
median17457
Q324498.25
95-th percentile38357.6
Maximum101794
Range100144
Interquartile range (IQR)12702.25

Descriptive statistics

Standard deviation10523.90488
Coefficient of variation (CV)0.5472368428
Kurtosis4.185816396
Mean19230.9875
Median Absolute Deviation (MAD)6251.5
Skewness1.425118906
Sum38461975
Variance110752574
MonotonicityNot monotonic
2024-03-25T13:34:13.279976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22328 3
 
0.1%
25692 3
 
0.1%
15333 3
 
0.1%
12582 3
 
0.1%
10955 3
 
0.1%
22242 3
 
0.1%
17830 3
 
0.1%
26079 2
 
0.1%
10588 2
 
0.1%
11088 2
 
0.1%
Other values (1850) 1973
98.7%
ValueCountFrequency (%)
1650 1
0.1%
1716 1
0.1%
1905 1
0.1%
1925 1
0.1%
1980 1
0.1%
ValueCountFrequency (%)
101794 1
0.1%
77884 1
0.1%
74997 1
0.1%
68300 2
0.1%
68169 1
0.1%

TRIAL_FIXATION_COUNT
Real number (ℝ)

Distinct240
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.992
Minimum9
Maximum396
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:13.376668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile35
Q166.75
median94
Q3129
95-th percentile193.05
Maximum396
Range387
Interquartile range (IQR)62.25

Descriptive statistics

Standard deviation50.87899638
Coefficient of variation (CV)0.4988528157
Kurtosis2.418134754
Mean101.992
Median Absolute Deviation (MAD)31
Skewness1.142440932
Sum203984
Variance2588.672272
MonotonicityNot monotonic
2024-03-25T13:34:13.475135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 30
 
1.5%
62 25
 
1.2%
84 25
 
1.2%
77 23
 
1.1%
73 23
 
1.1%
76 22
 
1.1%
96 22
 
1.1%
82 22
 
1.1%
110 22
 
1.1%
63 21
 
1.1%
Other values (230) 1765
88.2%
ValueCountFrequency (%)
9 2
0.1%
12 1
 
0.1%
13 3
0.1%
14 1
 
0.1%
15 1
 
0.1%
ValueCountFrequency (%)
396 1
0.1%
364 1
0.1%
359 2
0.1%
334 1
0.1%
326 2
0.1%

TRIAL_IA_COUNT
Real number (ℝ)

Distinct104
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.391
Minimum50
Maximum165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:13.577067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile71
Q191
median118
Q3138
95-th percentile158
Maximum165
Range115
Interquartile range (IQR)47

Descriptive statistics

Standard deviation27.61411575
Coefficient of variation (CV)0.2393090947
Kurtosis-1.001561069
Mean115.391
Median Absolute Deviation (MAD)23
Skewness-0.1421568127
Sum230782
Variance762.5393887
MonotonicityNot monotonic
2024-03-25T13:34:13.673095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134 64
 
3.2%
142 53
 
2.6%
121 53
 
2.6%
106 49
 
2.5%
82 46
 
2.3%
111 44
 
2.2%
123 43
 
2.1%
148 41
 
2.1%
159 37
 
1.8%
88 35
 
1.8%
Other values (94) 1535
76.8%
ValueCountFrequency (%)
50 5
0.2%
54 1
 
0.1%
56 4
0.2%
58 8
0.4%
61 9
0.4%
ValueCountFrequency (%)
165 19
0.9%
164 8
 
0.4%
163 5
 
0.2%
162 5
 
0.2%
160 24
1.2%
Distinct121
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.3995
Minimum8
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:13.767548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile29
Q148
median65
Q381
95-th percentile106
Maximum134
Range126
Interquartile range (IQR)33

Descriptive statistics

Standard deviation23.2057569
Coefficient of variation (CV)0.3548307999
Kurtosis-0.4408612597
Mean65.3995
Median Absolute Deviation (MAD)16
Skewness0.149643803
Sum130799
Variance538.5071533
MonotonicityNot monotonic
2024-03-25T13:34:13.864695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 41
 
2.1%
77 40
 
2.0%
56 39
 
1.9%
67 37
 
1.8%
60 37
 
1.8%
58 37
 
1.8%
75 36
 
1.8%
69 35
 
1.8%
48 35
 
1.8%
66 35
 
1.8%
Other values (111) 1628
81.4%
ValueCountFrequency (%)
8 1
0.1%
9 1
0.1%
10 2
0.1%
11 1
0.1%
12 1
0.1%
ValueCountFrequency (%)
134 2
0.1%
131 1
 
0.1%
129 1
 
0.1%
128 2
0.1%
125 3
0.1%
Distinct472
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:14.018618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length107
Median length76
Mean length57.3675
Min length11

Characters and Unicode

Total characters114735
Distinct characters76
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)1.7%

Sample

1st rowWhat changed between 2011 and 2012?
2nd rowHow would later start times affect families, according to Kelley?
3rd rowWhat does Pullman think about pirating?
4th rowWhat is one way in which big companies use the results of the Mercer Quality of Life study?
5th rowWhat is true of one of the meteorite fragments?
ValueCountFrequency (%)
the 1322
 
6.7%
what 1151
 
5.9%
to 667
 
3.4%
is 558
 
2.8%
of 531
 
2.7%
does 492
 
2.5%
in 469
 
2.4%
why 255
 
1.3%
do 250
 
1.3%
about 226
 
1.2%
Other values (1326) 13722
69.9%
2024-03-25T13:34:14.285224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17643
15.4%
e 10831
 
9.4%
t 8301
 
7.2%
a 7451
 
6.5%
o 7350
 
6.4%
i 6434
 
5.6%
s 6381
 
5.6%
n 5888
 
5.1%
r 5489
 
4.8%
h 5150
 
4.5%
Other values (66) 33817
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 88604
77.2%
Space Separator 17643
 
15.4%
Uppercase Letter 4588
 
4.0%
Other Punctuation 2710
 
2.4%
Decimal Number 872
 
0.8%
Dash Punctuation 198
 
0.2%
Initial Punctuation 54
 
< 0.1%
Final Punctuation 39
 
< 0.1%
Close Punctuation 9
 
< 0.1%
Open Punctuation 9
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10831
12.2%
t 8301
 
9.4%
a 7451
 
8.4%
o 7350
 
8.3%
i 6434
 
7.3%
s 6381
 
7.2%
n 5888
 
6.6%
r 5489
 
6.2%
h 5150
 
5.8%
d 3584
 
4.0%
Other values (16) 21745
24.5%
Uppercase Letter
ValueCountFrequency (%)
W 1706
37.2%
A 323
 
7.0%
H 242
 
5.3%
D 230
 
5.0%
P 212
 
4.6%
S 199
 
4.3%
C 185
 
4.0%
M 179
 
3.9%
I 170
 
3.7%
B 117
 
2.6%
Other values (15) 1025
22.3%
Decimal Number
ValueCountFrequency (%)
0 261
29.9%
1 236
27.1%
2 178
20.4%
5 47
 
5.4%
3 39
 
4.5%
8 29
 
3.3%
7 29
 
3.3%
4 28
 
3.2%
9 24
 
2.8%
6 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
? 1943
71.7%
' 366
 
13.5%
, 309
 
11.4%
. 73
 
2.7%
% 12
 
0.4%
# 7
 
0.3%
Initial Punctuation
ValueCountFrequency (%)
39
72.2%
15
 
27.8%
Currency Symbol
ValueCountFrequency (%)
£ 7
77.8%
$ 2
 
22.2%
Space Separator
ValueCountFrequency (%)
17643
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 198
100.0%
Final Punctuation
ValueCountFrequency (%)
39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 93192
81.2%
Common 21543
 
18.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10831
11.6%
t 8301
 
8.9%
a 7451
 
8.0%
o 7350
 
7.9%
i 6434
 
6.9%
s 6381
 
6.8%
n 5888
 
6.3%
r 5489
 
5.9%
h 5150
 
5.5%
d 3584
 
3.8%
Other values (41) 26333
28.3%
Common
ValueCountFrequency (%)
17643
81.9%
? 1943
 
9.0%
' 366
 
1.7%
, 309
 
1.4%
0 261
 
1.2%
1 236
 
1.1%
- 198
 
0.9%
2 178
 
0.8%
. 73
 
0.3%
5 47
 
0.2%
Other values (15) 289
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114635
99.9%
Punctuation 93
 
0.1%
None 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17643
15.4%
e 10831
 
9.4%
t 8301
 
7.2%
a 7451
 
6.5%
o 7350
 
6.4%
i 6434
 
5.6%
s 6381
 
5.6%
n 5888
 
5.1%
r 5489
 
4.8%
h 5150
 
4.5%
Other values (62) 33717
29.4%
Punctuation
ValueCountFrequency (%)
39
41.9%
39
41.9%
15
 
16.1%
None
ValueCountFrequency (%)
£ 7
100.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
False
1414 
True
586 
ValueCountFrequency (%)
False 1414
70.7%
True 586
29.3%
2024-03-25T13:34:14.401575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
False
1759 
True
241 
ValueCountFrequency (%)
False 1759
87.9%
True 241
 
12.0%
2024-03-25T13:34:14.479713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:14.537318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.4135
Min length5

Characters and Unicode

Total characters10827
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowother
2nd rowa_span
3rd rowa_span
4th rowother
5th rowother
ValueCountFrequency (%)
other 1173
58.7%
a_span 586
29.3%
d_span 241
 
12.0%
2024-03-25T13:34:14.693740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1413
13.1%
t 1173
10.8%
o 1173
10.8%
h 1173
10.8%
e 1173
10.8%
r 1173
10.8%
_ 827
7.6%
s 827
7.6%
p 827
7.6%
n 827
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10000
92.4%
Connector Punctuation 827
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1413
14.1%
t 1173
11.7%
o 1173
11.7%
h 1173
11.7%
e 1173
11.7%
r 1173
11.7%
s 827
8.3%
p 827
8.3%
n 827
8.3%
d 241
 
2.4%
Connector Punctuation
ValueCountFrequency (%)
_ 827
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10000
92.4%
Common 827
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1413
14.1%
t 1173
11.7%
o 1173
11.7%
h 1173
11.7%
e 1173
11.7%
r 1173
11.7%
s 827
8.3%
p 827
8.3%
n 827
8.3%
d 241
 
2.4%
Common
ValueCountFrequency (%)
_ 827
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1413
13.1%
t 1173
10.8%
o 1173
10.8%
h 1173
10.8%
e 1173
10.8%
r 1173
10.8%
_ 827
7.6%
s 827
7.6%
p 827
7.6%
n 827
7.6%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
False
1243 
True
757 
ValueCountFrequency (%)
False 1243
62.2%
True 757
37.9%
2024-03-25T13:34:14.789499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
False
1343 
True
657 
ValueCountFrequency (%)
False 1343
67.2%
True 657
32.9%
2024-03-25T13:34:14.867800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:14.930037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length19
Mean length18.4995
Min length16

Characters and Unicode

Total characters36999
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfter Critical Span
2nd rowIn Critical Span
3rd rowIn Critical Span
4th rowBefore Critical Span
5th rowBefore Critical Span
ValueCountFrequency (%)
critical 2000
33.3%
span 2000
33.3%
before 757
 
12.6%
after 657
 
10.9%
in 586
 
9.8%
2024-03-25T13:34:15.105107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 4000
10.8%
a 4000
10.8%
4000
10.8%
r 3414
9.2%
t 2657
 
7.2%
n 2586
 
7.0%
e 2171
 
5.9%
S 2000
 
5.4%
C 2000
 
5.4%
l 2000
 
5.4%
Other values (7) 8171
22.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26999
73.0%
Uppercase Letter 6000
 
16.2%
Space Separator 4000
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 4000
14.8%
a 4000
14.8%
r 3414
12.6%
t 2657
9.8%
n 2586
9.6%
e 2171
8.0%
l 2000
7.4%
p 2000
7.4%
c 2000
7.4%
f 1414
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
S 2000
33.3%
C 2000
33.3%
B 757
 
12.6%
A 657
 
10.9%
I 586
 
9.8%
Space Separator
ValueCountFrequency (%)
4000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32999
89.2%
Common 4000
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 4000
12.1%
a 4000
12.1%
r 3414
10.3%
t 2657
8.1%
n 2586
7.8%
e 2171
 
6.6%
S 2000
 
6.1%
C 2000
 
6.1%
l 2000
 
6.1%
p 2000
 
6.1%
Other values (6) 6171
18.7%
Common
ValueCountFrequency (%)
4000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36999
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 4000
10.8%
a 4000
10.8%
4000
10.8%
r 3414
9.2%
t 2657
 
7.2%
n 2586
 
7.0%
e 2171
 
5.9%
S 2000
 
5.4%
C 2000
 
5.4%
l 2000
 
5.4%
Other values (7) 8171
22.1%

is_correct
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
True
1657 
False
343 
ValueCountFrequency (%)
True 1657
82.8%
False 343
 
17.2%
2024-03-25T13:34:15.202944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:15.253518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowB
ValueCountFrequency (%)
a 1657
82.8%
b 191
 
9.6%
c 106
 
5.3%
d 46
 
2.3%
2024-03-25T13:34:15.393299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1657
82.8%
B 191
 
9.6%
C 106
 
5.3%
D 46
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1657
82.8%
B 191
 
9.6%
C 106
 
5.3%
D 46
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 2000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1657
82.8%
B 191
 
9.6%
C 106
 
5.3%
D 46
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1657
82.8%
B 191
 
9.6%
C 106
 
5.3%
D 46
 
2.3%
Distinct321
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:15.539318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.1035
Min length9

Characters and Unicode

Total characters18207
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.3%

Sample

1st row2_3_Ele_1
2nd row1_9_Ele_6
3rd row1_5_Adv_1
4th row2_6_Adv_1
5th row3_8_Adv_3
ValueCountFrequency (%)
3_3_adv_2 15
 
0.8%
3_2_adv_5 14
 
0.7%
2_5_adv_2 14
 
0.7%
2_3_adv_2 14
 
0.7%
2_6_adv_1 14
 
0.7%
1_7_ele_1 13
 
0.7%
1_5_adv_2 13
 
0.7%
3_4_adv_1 13
 
0.7%
2_6_adv_4 12
 
0.6%
2_2_adv_5 12
 
0.6%
Other values (311) 1866
93.3%
2024-03-25T13:34:15.781351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 6000
33.0%
1 1469
 
8.1%
3 1252
 
6.9%
2 1212
 
6.7%
A 1112
 
6.1%
v 1112
 
6.1%
d 1112
 
6.1%
E 888
 
4.9%
l 888
 
4.9%
e 888
 
4.9%
Other values (7) 2274
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6207
34.1%
Connector Punctuation 6000
33.0%
Lowercase Letter 4000
22.0%
Uppercase Letter 2000
 
11.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1469
23.7%
3 1252
20.2%
2 1212
19.5%
4 571
 
9.2%
5 481
 
7.7%
6 329
 
5.3%
7 274
 
4.4%
8 226
 
3.6%
0 207
 
3.3%
9 186
 
3.0%
Lowercase Letter
ValueCountFrequency (%)
v 1112
27.8%
d 1112
27.8%
l 888
22.2%
e 888
22.2%
Uppercase Letter
ValueCountFrequency (%)
A 1112
55.6%
E 888
44.4%
Connector Punctuation
ValueCountFrequency (%)
_ 6000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12207
67.0%
Latin 6000
33.0%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 6000
49.2%
1 1469
 
12.0%
3 1252
 
10.3%
2 1212
 
9.9%
4 571
 
4.7%
5 481
 
3.9%
6 329
 
2.7%
7 274
 
2.2%
8 226
 
1.9%
0 207
 
1.7%
Latin
ValueCountFrequency (%)
A 1112
18.5%
v 1112
18.5%
d 1112
18.5%
E 888
14.8%
l 888
14.8%
e 888
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 6000
33.0%
1 1469
 
8.1%
3 1252
 
6.9%
2 1212
 
6.7%
A 1112
 
6.1%
v 1112
 
6.1%
d 1112
 
6.1%
E 888
 
4.9%
l 888
 
4.9%
e 888
 
4.9%
Other values (7) 2274
 
12.5%
Distinct360
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:15.963332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.714
Min length5

Characters and Unicode

Total characters13428
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.4%

Sample

1st rowl8_185
2nd rowl33_284
3rd rowl7_71
4th rowl5_175
5th rowl4_331
ValueCountFrequency (%)
l30_61 12
 
0.6%
l23_368 12
 
0.6%
l60_505 11
 
0.5%
l27_378 11
 
0.5%
l8_268 11
 
0.5%
l1_64 11
 
0.5%
l7_71 11
 
0.5%
l55_500 11
 
0.5%
l45_135 10
 
0.5%
l23_235 10
 
0.5%
Other values (350) 1890
94.5%
2024-03-25T13:34:16.232654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 2000
14.9%
l 1991
14.8%
3 1382
10.3%
1 1297
9.7%
2 1277
9.5%
5 1234
9.2%
4 1230
9.2%
0 622
 
4.6%
6 610
 
4.5%
8 609
 
4.5%
Other values (3) 1176
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9428
70.2%
Connector Punctuation 2000
 
14.9%
Lowercase Letter 1991
 
14.8%
Uppercase Letter 9
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1382
14.7%
1 1297
13.8%
2 1277
13.5%
5 1234
13.1%
4 1230
13.0%
0 622
6.6%
6 610
6.5%
8 609
6.5%
9 592
6.3%
7 575
6.1%
Connector Punctuation
ValueCountFrequency (%)
_ 2000
100.0%
Lowercase Letter
ValueCountFrequency (%)
l 1991
100.0%
Uppercase Letter
ValueCountFrequency (%)
L 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11428
85.1%
Latin 2000
 
14.9%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 2000
17.5%
3 1382
12.1%
1 1297
11.3%
2 1277
11.2%
5 1234
10.8%
4 1230
10.8%
0 622
 
5.4%
6 610
 
5.3%
8 609
 
5.3%
9 592
 
5.2%
Latin
ValueCountFrequency (%)
l 1991
99.6%
L 9
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 2000
14.9%
l 1991
14.8%
3 1382
10.3%
1 1297
9.7%
2 1277
9.5%
5 1234
9.2%
4 1230
9.2%
0 622
 
4.6%
6 610
 
4.5%
8 609
 
4.5%
Other values (3) 1176
8.8%

total_IA_DWELL_TIME
Real number (ℝ)

Distinct1859
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19029.403
Minimum1525
Maximum99018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:16.347076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1525
5-th percentile5987.85
Q111741
median17274.5
Q324149.25
95-th percentile37965.9
Maximum99018
Range97493
Interquartile range (IQR)12408.25

Descriptive statistics

Standard deviation10400.33483
Coefficient of variation (CV)0.5465402582
Kurtosis4.02598308
Mean19029.403
Median Absolute Deviation (MAD)6137.5
Skewness1.407584873
Sum38058806
Variance108166964.6
MonotonicityNot monotonic
2024-03-25T13:34:16.446045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12582 3
 
0.1%
10955 3
 
0.1%
13716 3
 
0.1%
22328 3
 
0.1%
15115 3
 
0.1%
3674 3
 
0.1%
13828 3
 
0.1%
25692 3
 
0.1%
21118 3
 
0.1%
9502 2
 
0.1%
Other values (1849) 1971
98.6%
ValueCountFrequency (%)
1525 1
0.1%
1650 1
0.1%
1905 1
0.1%
1925 1
0.1%
1980 1
0.1%
ValueCountFrequency (%)
99018 1
0.1%
76475 1
0.1%
73544 1
0.1%
68139 2
0.1%
67824 1
0.1%

min_IA_ID
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros2000
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:16.521999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2024-03-25T13:34:16.581857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 2000
100.0%
ValueCountFrequency (%)
0 2000
100.0%
ValueCountFrequency (%)
0 2000
100.0%

max_IA_ID
Real number (ℝ)

Distinct104
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.391
Minimum49
Maximum164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:16.663226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile70
Q190
median117
Q3137
95-th percentile157
Maximum164
Range115
Interquartile range (IQR)47

Descriptive statistics

Standard deviation27.61411575
Coefficient of variation (CV)0.241401122
Kurtosis-1.001561069
Mean114.391
Median Absolute Deviation (MAD)23
Skewness-0.1421568127
Sum228782
Variance762.5393887
MonotonicityNot monotonic
2024-03-25T13:34:16.760506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133 64
 
3.2%
141 53
 
2.6%
120 53
 
2.6%
105 49
 
2.5%
81 46
 
2.3%
110 44
 
2.2%
122 43
 
2.1%
147 41
 
2.1%
158 37
 
1.8%
87 35
 
1.8%
Other values (94) 1535
76.8%
ValueCountFrequency (%)
49 5
0.2%
53 1
 
0.1%
55 4
0.2%
57 8
0.4%
60 9
0.4%
ValueCountFrequency (%)
164 19
0.9%
163 8
 
0.4%
162 5
 
0.2%
161 5
 
0.2%
159 24
1.2%

part_total_IA_DWELL_TIME
Real number (ℝ)

Distinct1845
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9256.943
Minimum0
Maximum70795
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:16.856998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1471
Q14238
median7808.5
Q312393.25
95-th percentile22334.75
Maximum70795
Range70795
Interquartile range (IQR)8155.25

Descriptive statistics

Standard deviation6977.053245
Coefficient of variation (CV)0.7537102956
Kurtosis6.963200146
Mean9256.943
Median Absolute Deviation (MAD)3865.5
Skewness1.903814132
Sum18513886
Variance48679271.98
MonotonicityNot monotonic
2024-03-25T13:34:17.245055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
0.2%
4548 3
 
0.1%
6904 3
 
0.1%
5613 3
 
0.1%
17954 3
 
0.1%
6882 3
 
0.1%
10556 3
 
0.1%
3696 3
 
0.1%
23576 3
 
0.1%
14475 3
 
0.1%
Other values (1835) 1968
98.4%
ValueCountFrequency (%)
0 5
0.2%
31 1
 
0.1%
103 1
 
0.1%
119 1
 
0.1%
222 1
 
0.1%
ValueCountFrequency (%)
70795 1
0.1%
54391 1
0.1%
48339 1
0.1%
47814 1
0.1%
47205 1
0.1%

part_min_IA_ID
Real number (ℝ)

ZEROS 

Distinct123
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.6245
Minimum0
Maximum148
Zeros869
Zeros (%)43.5%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:17.346011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q352
95-th percentile91
Maximum148
Range148
Interquartile range (IQR)52

Descriptive statistics

Standard deviation32.62501641
Coefficient of variation (CV)1.101284964
Kurtosis-0.1676014489
Mean29.6245
Median Absolute Deviation (MAD)23
Skewness0.8297411947
Sum59249
Variance1064.391696
MonotonicityNot monotonic
2024-03-25T13:34:17.438584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 869
43.5%
35 30
 
1.5%
38 30
 
1.5%
50 28
 
1.4%
36 28
 
1.4%
39 26
 
1.3%
43 23
 
1.1%
52 23
 
1.1%
45 23
 
1.1%
51 22
 
1.1%
Other values (113) 898
44.9%
ValueCountFrequency (%)
0 869
43.5%
1 4
 
0.2%
3 1
 
0.1%
4 3
 
0.1%
5 1
 
0.1%
ValueCountFrequency (%)
148 3
0.1%
141 1
 
0.1%
135 1
 
0.1%
131 3
0.1%
130 1
 
0.1%

part_max_IA_ID
Real number (ℝ)

Distinct155
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.776
Minimum6
Maximum164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:17.537784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile29.95
Q158
median82
Q3114.25
95-th percentile150
Maximum164
Range158
Interquartile range (IQR)56.25

Descriptive statistics

Standard deviation36.58209875
Coefficient of variation (CV)0.4264840835
Kurtosis-0.8433443678
Mean85.776
Median Absolute Deviation (MAD)27
Skewness0.1821239006
Sum171552
Variance1338.249949
MonotonicityNot monotonic
2024-03-25T13:34:17.633399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79 37
 
1.8%
69 34
 
1.7%
141 33
 
1.7%
105 32
 
1.6%
83 32
 
1.6%
68 31
 
1.6%
81 30
 
1.5%
71 30
 
1.5%
89 29
 
1.5%
90 28
 
1.4%
Other values (145) 1684
84.2%
ValueCountFrequency (%)
6 1
0.1%
7 1
0.1%
8 2
0.1%
10 2
0.1%
11 1
0.1%
ValueCountFrequency (%)
164 4
0.2%
163 2
 
0.1%
162 4
0.2%
161 2
 
0.1%
159 6
0.3%

start_of_line
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
False
1859 
True
 
141
ValueCountFrequency (%)
False 1859
93.0%
True 141
 
7.0%
2024-03-25T13:34:17.733442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

end_of_line
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
False
1888 
True
 
112
ValueCountFrequency (%)
False 1888
94.4%
True 112
 
5.6%
2024-03-25T13:34:17.808845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length
Real number (ℝ)

Distinct17
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.522
Minimum0
Maximum16
Zeros7
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:17.873007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q36
95-th percentile9
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.430940299
Coefficient of variation (CV)0.5375807825
Kurtosis1.096729389
Mean4.522
Median Absolute Deviation (MAD)1
Skewness1.04392977
Sum9044
Variance5.909470735
MonotonicityNot monotonic
2024-03-25T13:34:17.947038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 433
21.6%
4 357
17.8%
2 326
16.3%
5 214
10.7%
6 203
10.2%
7 157
 
7.8%
8 86
 
4.3%
9 68
 
3.4%
1 68
 
3.4%
10 31
 
1.6%
Other values (7) 57
 
2.9%
ValueCountFrequency (%)
0 7
 
0.4%
1 68
 
3.4%
2 326
16.3%
3 433
21.6%
4 357
17.8%
ValueCountFrequency (%)
16 1
 
0.1%
15 1
 
0.1%
14 2
 
0.1%
13 11
0.5%
12 13
0.7%

Wordfreq_Frequency
Real number (ℝ)

Distinct396
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.96382842
Minimum4.218934102
Maximum36.54120904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:18.040003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.218934102
5-th percentile4.218934102
Q16.810358853
median10.09867856
Q313.78589111
95-th percentile19.16951087
Maximum36.54120904
Range32.32227494
Interquartile range (IQR)6.975532261

Descriptive statistics

Standard deviation5.39171363
Coefficient of variation (CV)0.4917728937
Kurtosis5.181089209
Mean10.96382842
Median Absolute Deviation (MAD)3.483391518
Skewness1.659219994
Sum21927.65685
Variance29.07057587
MonotonicityNot monotonic
2024-03-25T13:34:18.130031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.218934102 136
 
6.8%
5.28208783 60
 
3.0%
5.448508591 58
 
2.9%
5.216250017 58
 
2.9%
6.615287038 46
 
2.3%
5.316168826 46
 
2.3%
6.41734766 41
 
2.1%
5.748553568 36
 
1.8%
7.871548215 26
 
1.3%
36.54120904 24
 
1.2%
Other values (386) 1469
73.5%
ValueCountFrequency (%)
4.218934102 136
6.8%
5.216250017 58
2.9%
5.28208783 60
3.0%
5.316168826 46
 
2.3%
5.448508591 58
2.9%
ValueCountFrequency (%)
36.54120904 24
1.2%
35.7841858 1
 
0.1%
34.5091082 1
 
0.1%
31.73385412 1
 
0.1%
29.11494429 1
 
0.1%

subtlex_Frequency
Real number (ℝ)

ZEROS 

Distinct733
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.959227184
Minimum0
Maximum25.56730941
Zeros147
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:18.227299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.186248136
median9.184145149
Q314.06435914
95-th percentile18.81445401
Maximum25.56730941
Range25.56730941
Interquartile range (IQR)7.878111

Descriptive statistics

Standard deviation5.212790248
Coefficient of variation (CV)0.5234131275
Kurtosis-0.4451595502
Mean9.959227184
Median Absolute Deviation (MAD)3.606622427
Skewness0.1594390791
Sum19918.45437
Variance27.17318217
MonotonicityNot monotonic
2024-03-25T13:34:18.326581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 147
 
7.3%
5.048944398 136
 
6.8%
6.186248136 60
 
3.0%
5.577522722 58
 
2.9%
5.425888255 58
 
2.9%
6.395880917 46
 
2.3%
6.757092393 41
 
2.1%
6.640237508 36
 
1.8%
6.110319383 24
 
1.2%
7.143528718 22
 
1.1%
Other values (723) 1372
68.6%
ValueCountFrequency (%)
0 147
7.3%
2.080276659 1
 
0.1%
2.983439382 1
 
0.1%
4.54169872 3
 
0.1%
4.597300235 1
 
0.1%
ValueCountFrequency (%)
25.56730941 2
0.1%
24.56730941 2
0.1%
23.98234691 2
0.1%
23.24538131 2
0.1%
22.98234691 2
0.1%

gpt2_Surprisal
Real number (ℝ)

Distinct1942
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.155008373
Minimum0.0005241425533
Maximum58.80669975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:18.424127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0005241425533
5-th percentile0.4551699936
Q12.366586998
median4.746567011
Q38.537883759
95-th percentile15.75480716
Maximum58.80669975
Range58.80617561
Interquartile range (IQR)6.17129676

Descriptive statistics

Standard deviation5.539675228
Coefficient of variation (CV)0.9000272448
Kurtosis13.39991443
Mean6.155008373
Median Absolute Deviation (MAD)2.805335999
Skewness2.585042912
Sum12310.01675
Variance30.68800163
MonotonicityNot monotonic
2024-03-25T13:34:18.516625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.863378048 3
 
0.1%
7.999939442 2
 
0.1%
4.299787998 2
 
0.1%
7.169922829 2
 
0.1%
8.336687088 2
 
0.1%
4.262489319 2
 
0.1%
5.608290672 2
 
0.1%
1.188468933 2
 
0.1%
4.556834221 2
 
0.1%
1.288764238 2
 
0.1%
Other values (1932) 1979
99.0%
ValueCountFrequency (%)
0.0005241425533 1
0.1%
0.001739758998 1
0.1%
0.00205871067 1
0.1%
0.004684074316 1
0.1%
0.006103119347 1
0.1%
ValueCountFrequency (%)
58.80669975 1
0.1%
57.27803421 1
0.1%
50.08077455 1
0.1%
39.70263004 1
0.1%
37.34407115 1
0.1%

Word_idx
Real number (ℝ)

Distinct152
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.886
Minimum1
Maximum154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:18.616070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q128
median56
Q385
95-th percentile126
Maximum154
Range153
Interquartile range (IQR)57

Descriptive statistics

Standard deviation36.83494822
Coefficient of variation (CV)0.6255298072
Kurtosis-0.6944202222
Mean58.886
Median Absolute Deviation (MAD)28
Skewness0.3883048174
Sum117772
Variance1356.813411
MonotonicityNot monotonic
2024-03-25T13:34:18.710766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 29
 
1.5%
35 26
 
1.3%
19 25
 
1.2%
45 25
 
1.2%
79 24
 
1.2%
34 24
 
1.2%
26 23
 
1.1%
59 23
 
1.1%
6 23
 
1.1%
28 23
 
1.1%
Other values (142) 1755
87.8%
ValueCountFrequency (%)
1 22
1.1%
2 20
1.0%
3 21
1.1%
4 18
0.9%
5 11
0.5%
ValueCountFrequency (%)
154 2
0.1%
153 1
 
0.1%
152 1
 
0.1%
150 1
 
0.1%
149 4
0.2%

Token
Text

Distinct996
Distinct (%)49.8%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:18.883177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length18
Median length15
Mean length4.7065
Min length1

Characters and Unicode

Total characters9413
Distinct characters75
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique749 ?
Unique (%)37.5%

Sample

1st rowOberg
2nd rowearly.
3rd rowto
4th rowin
5th rowthe
ValueCountFrequency (%)
the 136
 
6.8%
and 60
 
3.0%
a 58
 
2.9%
to 58
 
2.9%
of 46
 
2.3%
is 41
 
2.1%
in 36
 
1.8%
that 24
 
1.2%
for 22
 
1.1%
are 21
 
1.1%
Other values (882) 1498
74.9%
2024-03-25T13:34:19.152281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1109
 
11.8%
t 758
 
8.1%
a 751
 
8.0%
o 643
 
6.8%
n 602
 
6.4%
i 597
 
6.3%
s 547
 
5.8%
r 546
 
5.8%
h 445
 
4.7%
l 360
 
3.8%
Other values (65) 3055
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8590
91.3%
Other Punctuation 323
 
3.4%
Uppercase Letter 278
 
3.0%
Decimal Number 172
 
1.8%
Dash Punctuation 34
 
0.4%
Currency Symbol 6
 
0.1%
Open Punctuation 5
 
0.1%
Close Punctuation 5
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1109
12.9%
t 758
 
8.8%
a 751
 
8.7%
o 643
 
7.5%
n 602
 
7.0%
i 597
 
6.9%
s 547
 
6.4%
r 546
 
6.4%
h 445
 
5.2%
l 360
 
4.2%
Other values (16) 2232
26.0%
Uppercase Letter
ValueCountFrequency (%)
T 32
 
11.5%
I 25
 
9.0%
A 21
 
7.6%
B 20
 
7.2%
H 16
 
5.8%
S 16
 
5.8%
O 13
 
4.7%
F 13
 
4.7%
P 12
 
4.3%
K 11
 
4.0%
Other values (15) 99
35.6%
Decimal Number
ValueCountFrequency (%)
0 49
28.5%
1 47
27.3%
2 19
 
11.0%
3 13
 
7.6%
5 11
 
6.4%
4 10
 
5.8%
8 9
 
5.2%
9 6
 
3.5%
7 5
 
2.9%
6 3
 
1.7%
Other Punctuation
ValueCountFrequency (%)
, 127
39.3%
. 105
32.5%
" 49
 
15.2%
' 31
 
9.6%
% 4
 
1.2%
; 3
 
0.9%
? 2
 
0.6%
: 2
 
0.6%
Currency Symbol
ValueCountFrequency (%)
£ 3
50.0%
$ 2
33.3%
1
 
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8868
94.2%
Common 545
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1109
12.5%
t 758
 
8.5%
a 751
 
8.5%
o 643
 
7.3%
n 602
 
6.8%
i 597
 
6.7%
s 547
 
6.2%
r 546
 
6.2%
h 445
 
5.0%
l 360
 
4.1%
Other values (41) 2510
28.3%
Common
ValueCountFrequency (%)
, 127
23.3%
. 105
19.3%
0 49
 
9.0%
" 49
 
9.0%
1 47
 
8.6%
- 34
 
6.2%
' 31
 
5.7%
2 19
 
3.5%
3 13
 
2.4%
5 11
 
2.0%
Other values (14) 60
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9409
> 99.9%
None 3
 
< 0.1%
Currency Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1109
 
11.8%
t 758
 
8.1%
a 751
 
8.0%
o 643
 
6.8%
n 602
 
6.4%
i 597
 
6.3%
s 547
 
5.8%
r 546
 
5.8%
h 445
 
4.7%
l 360
 
3.8%
Other values (63) 3051
32.4%
None
ValueCountFrequency (%)
£ 3
100.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%

POS
Text

Distinct15
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:19.252349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.7055
Min length3

Characters and Unicode

Total characters7411
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPROPN
2nd rowADV
3rd rowPART
4th rowADP
5th rowDET
ValueCountFrequency (%)
noun 413
20.6%
verb 275
13.8%
det 224
11.2%
adp 213
10.7%
aux 149
 
7.4%
pron 145
 
7.2%
propn 139
 
7.0%
adj 137
 
6.9%
cconj 85
 
4.2%
adv 71
 
3.5%
Other values (5) 149
 
7.4%
2024-03-25T13:34:19.432427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 1295
17.5%
O 817
11.0%
P 689
9.3%
D 645
8.7%
U 627
8.5%
A 616
8.3%
R 605
8.2%
E 499
 
6.7%
V 346
 
4.7%
T 277
 
3.7%
Other values (7) 995
13.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7411
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1295
17.5%
O 817
11.0%
P 689
9.3%
D 645
8.7%
U 627
8.5%
A 616
8.3%
R 605
8.2%
E 499
 
6.7%
V 346
 
4.7%
T 277
 
3.7%
Other values (7) 995
13.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 7411
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1295
17.5%
O 817
11.0%
P 689
9.3%
D 645
8.7%
U 627
8.5%
A 616
8.3%
R 605
8.2%
E 499
 
6.7%
V 346
 
4.7%
T 277
 
3.7%
Other values (7) 995
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7411
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1295
17.5%
O 817
11.0%
P 689
9.3%
D 645
8.7%
U 627
8.5%
A 616
8.3%
R 605
8.2%
E 499
 
6.7%
V 346
 
4.7%
T 277
 
3.7%
Other values (7) 995
13.4%

TAG
Text

Distinct32
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:19.533627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.387
Min length1

Characters and Unicode

Total characters4774
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNNP
2nd rowRB
3rd rowTO
4th rowIN
5th rowDT
ValueCountFrequency (%)
nn 270
13.5%
in 236
11.8%
dt 228
11.4%
nns 147
 
7.3%
nnp 136
 
6.8%
jj 125
 
6.2%
prp 99
 
5.0%
cc 85
 
4.2%
vbz 81
 
4.0%
vb 77
 
3.9%
Other values (20) 516
25.8%
2024-03-25T13:34:19.720534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 1411
29.6%
B 486
 
10.2%
P 417
 
8.7%
D 406
 
8.5%
V 402
 
8.4%
T 290
 
6.1%
J 274
 
5.7%
I 236
 
4.9%
C 228
 
4.8%
R 199
 
4.2%
Other values (10) 425
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4742
99.3%
Currency Symbol 25
 
0.5%
Other Punctuation 7
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1411
29.8%
B 486
 
10.2%
P 417
 
8.8%
D 406
 
8.6%
V 402
 
8.5%
T 290
 
6.1%
J 274
 
5.8%
I 236
 
5.0%
C 228
 
4.8%
R 199
 
4.2%
Other values (8) 393
 
8.3%
Currency Symbol
ValueCountFrequency (%)
$ 25
100.0%
Other Punctuation
ValueCountFrequency (%)
: 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4742
99.3%
Common 32
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1411
29.8%
B 486
 
10.2%
P 417
 
8.8%
D 406
 
8.6%
V 402
 
8.5%
T 290
 
6.1%
J 274
 
5.8%
I 236
 
5.0%
C 228
 
4.8%
R 199
 
4.2%
Other values (8) 393
 
8.3%
Common
ValueCountFrequency (%)
$ 25
78.1%
: 7
 
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1411
29.6%
B 486
 
10.2%
P 417
 
8.7%
D 406
 
8.5%
V 402
 
8.4%
T 290
 
6.1%
J 274
 
5.7%
I 236
 
4.9%
C 228
 
4.8%
R 199
 
4.2%
Other values (10) 425
 
8.9%

Token_idx
Real number (ℝ)

ZEROS 

Distinct152
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.886
Minimum0
Maximum153
Zeros22
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:19.830550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q127
median55
Q384
95-th percentile125
Maximum153
Range153
Interquartile range (IQR)57

Descriptive statistics

Standard deviation36.83494822
Coefficient of variation (CV)0.6363360437
Kurtosis-0.6944202222
Mean57.886
Median Absolute Deviation (MAD)28
Skewness0.3883048174
Sum115772
Variance1356.813411
MonotonicityNot monotonic
2024-03-25T13:34:19.926592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 29
 
1.5%
34 26
 
1.3%
18 25
 
1.2%
44 25
 
1.2%
78 24
 
1.2%
33 24
 
1.2%
25 23
 
1.1%
58 23
 
1.1%
5 23
 
1.1%
27 23
 
1.1%
Other values (142) 1755
87.8%
ValueCountFrequency (%)
0 22
1.1%
1 20
1.0%
2 21
1.1%
3 18
0.9%
4 11
0.5%
ValueCountFrequency (%)
153 2
0.1%
152 1
 
0.1%
151 1
 
0.1%
149 1
 
0.1%
148 4
0.2%
Distinct41
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:20.034910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.403
Min length2

Characters and Unicode

Total characters8806
Distinct characters24
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st rownsubj
2nd rowadvmod
3rd rowaux
4th rowprep
5th rowdet
ValueCountFrequency (%)
det 217
 
10.8%
prep 201
 
10.1%
nsubj 198
 
9.9%
pobj 191
 
9.6%
root 126
 
6.3%
amod 115
 
5.8%
compound 99
 
5.0%
aux 96
 
4.8%
dobj 88
 
4.4%
cc 85
 
4.2%
Other values (31) 584
29.2%
2024-03-25T13:34:20.229995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 987
11.2%
p 982
11.2%
d 774
 
8.8%
c 611
 
6.9%
j 577
 
6.6%
m 541
 
6.1%
a 507
 
5.8%
b 505
 
5.7%
u 493
 
5.6%
e 475
 
5.4%
Other values (14) 2354
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8302
94.3%
Uppercase Letter 504
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 987
11.9%
p 982
11.8%
d 774
9.3%
c 611
 
7.4%
j 577
 
7.0%
m 541
 
6.5%
a 507
 
6.1%
b 505
 
6.1%
u 493
 
5.9%
e 475
 
5.7%
Other values (11) 1850
22.3%
Uppercase Letter
ValueCountFrequency (%)
O 252
50.0%
T 126
25.0%
R 126
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8806
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 987
11.2%
p 982
11.2%
d 774
 
8.8%
c 611
 
6.9%
j 577
 
6.6%
m 541
 
6.1%
a 507
 
5.8%
b 505
 
5.7%
u 493
 
5.6%
e 475
 
5.4%
Other values (14) 2354
26.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 987
11.2%
p 982
11.2%
d 774
 
8.8%
c 611
 
6.9%
j 577
 
6.6%
m 541
 
6.1%
a 507
 
5.8%
b 505
 
5.7%
u 493
 
5.6%
e 475
 
5.4%
Other values (14) 2354
26.7%

Morph
Text

Distinct46
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:20.339547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length70
Median length58
Mean length21.8105
Min length2

Characters and Unicode

Total characters43621
Distinct characters42
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st row['Number=Sing']
2nd row[]
3rd row[]
4th row[]
5th row['Definite=Def', 'PronType=Art']
ValueCountFrequency (%)
number=sing 569
17.3%
409
12.4%
verbform=fin 240
 
7.3%
prontype=art 199
 
6.0%
number=plur 188
 
5.7%
tense=pres 183
 
5.5%
person=3 175
 
5.3%
tense=past 140
 
4.2%
definite=def 136
 
4.1%
degree=pos 126
 
3.8%
Other values (24) 933
28.3%
2024-03-25T13:34:20.547015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 5778
 
13.2%
e 4057
 
9.3%
r 3261
 
7.5%
= 2889
 
6.6%
n 2252
 
5.2%
] 2000
 
4.6%
[ 2000
 
4.6%
o 1539
 
3.5%
P 1489
 
3.4%
m 1424
 
3.3%
Other values (32) 16932
38.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21692
49.7%
Other Punctuation 7076
 
16.2%
Uppercase Letter 6469
 
14.8%
Math Symbol 2889
 
6.6%
Close Punctuation 2000
 
4.6%
Open Punctuation 2000
 
4.6%
Space Separator 1298
 
3.0%
Decimal Number 197
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4057
18.7%
r 3261
15.0%
n 2252
10.4%
o 1539
 
7.1%
m 1424
 
6.6%
s 1335
 
6.2%
i 1212
 
5.6%
b 1181
 
5.4%
u 1032
 
4.8%
t 772
 
3.6%
Other values (9) 3627
16.7%
Uppercase Letter
ValueCountFrequency (%)
P 1489
23.0%
N 910
14.1%
T 787
12.2%
F 670
10.4%
S 577
 
8.9%
D 494
 
7.6%
V 424
 
6.6%
C 315
 
4.9%
A 308
 
4.8%
I 268
 
4.1%
Other values (4) 227
 
3.5%
Decimal Number
ValueCountFrequency (%)
3 175
88.8%
1 18
 
9.1%
2 4
 
2.0%
Other Punctuation
ValueCountFrequency (%)
' 5778
81.7%
, 1298
 
18.3%
Math Symbol
ValueCountFrequency (%)
= 2889
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2000
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2000
100.0%
Space Separator
ValueCountFrequency (%)
1298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28161
64.6%
Common 15460
35.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4057
14.4%
r 3261
 
11.6%
n 2252
 
8.0%
o 1539
 
5.5%
P 1489
 
5.3%
m 1424
 
5.1%
s 1335
 
4.7%
i 1212
 
4.3%
b 1181
 
4.2%
u 1032
 
3.7%
Other values (23) 9379
33.3%
Common
ValueCountFrequency (%)
' 5778
37.4%
= 2889
18.7%
] 2000
 
12.9%
[ 2000
 
12.9%
1298
 
8.4%
, 1298
 
8.4%
3 175
 
1.1%
1 18
 
0.1%
2 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43621
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 5778
 
13.2%
e 4057
 
9.3%
r 3261
 
7.5%
= 2889
 
6.6%
n 2252
 
5.2%
] 2000
 
4.6%
[ 2000
 
4.6%
o 1539
 
3.5%
P 1489
 
3.4%
m 1424
 
3.3%
Other values (32) 16932
38.8%

Entity
Text

MISSING 

Distinct16
Distinct (%)5.8%
Missing1725
Missing (%)86.2%
Memory size31.2 KiB
2024-03-25T13:34:20.645738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length11
Median length8
Mean length4.789090909
Min length3

Characters and Unicode

Total characters1317
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)1.5%

Sample

1st rowPERSON
2nd rowGPE
3rd rowNORP
4th rowTIME
5th rowORG
ValueCountFrequency (%)
date 57
20.7%
org 56
20.4%
person 40
14.5%
cardinal 36
13.1%
gpe 25
9.1%
time 14
 
5.1%
quantity 12
 
4.4%
norp 9
 
3.3%
money 9
 
3.3%
loc 6
 
2.2%
Other values (6) 11
 
4.0%
2024-03-25T13:34:20.835053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 155
11.8%
R 149
11.3%
A 146
11.1%
O 124
9.4%
N 112
8.5%
T 102
7.7%
D 95
7.2%
G 81
 
6.2%
P 79
 
6.0%
I 63
 
4.8%
Other values (12) 211
16.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1315
99.8%
Connector Punctuation 2
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 155
11.8%
R 149
11.3%
A 146
11.1%
O 124
9.4%
N 112
8.5%
T 102
7.8%
D 95
7.2%
G 81
 
6.2%
P 79
 
6.0%
I 63
 
4.8%
Other values (11) 209
15.9%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1315
99.8%
Common 2
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 155
11.8%
R 149
11.3%
A 146
11.1%
O 124
9.4%
N 112
8.5%
T 102
7.8%
D 95
7.2%
G 81
 
6.2%
P 79
 
6.0%
I 63
 
4.8%
Other values (11) 209
15.9%
Common
ValueCountFrequency (%)
_ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 155
11.8%
R 149
11.3%
A 146
11.1%
O 124
9.4%
N 112
8.5%
T 102
7.7%
D 95
7.2%
G 81
 
6.2%
P 79
 
6.0%
I 63
 
4.8%
Other values (12) 211
16.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
True
1035 
False
965 
ValueCountFrequency (%)
True 1035
51.7%
False 965
48.2%
2024-03-25T13:34:20.935326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:20.994548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length4
Mean length3.9005
Min length3

Characters and Unicode

Total characters7801
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNOUN
2nd rowADJ
3rd rowFUNC
4th rowFUNC
5th rowFUNC
ValueCountFrequency (%)
func 962
48.1%
noun 552
27.6%
verb 275
 
13.8%
adj 208
 
10.4%
unknown 3
 
0.1%
2024-03-25T13:34:21.160444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 2075
26.6%
U 1517
19.4%
F 962
12.3%
C 962
12.3%
O 555
 
7.1%
V 275
 
3.5%
E 275
 
3.5%
R 275
 
3.5%
B 275
 
3.5%
A 208
 
2.7%
Other values (4) 422
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7801
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 2075
26.6%
U 1517
19.4%
F 962
12.3%
C 962
12.3%
O 555
 
7.1%
V 275
 
3.5%
E 275
 
3.5%
R 275
 
3.5%
B 275
 
3.5%
A 208
 
2.7%
Other values (4) 422
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 7801
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 2075
26.6%
U 1517
19.4%
F 962
12.3%
C 962
12.3%
O 555
 
7.1%
V 275
 
3.5%
E 275
 
3.5%
R 275
 
3.5%
B 275
 
3.5%
A 208
 
2.7%
Other values (4) 422
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 2075
26.6%
U 1517
19.4%
F 962
12.3%
C 962
12.3%
O 555
 
7.1%
V 275
 
3.5%
E 275
 
3.5%
R 275
 
3.5%
B 275
 
3.5%
A 208
 
2.7%
Other values (4) 422
 
5.4%

Head_word_idx
Real number (ℝ)

Distinct153
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.696
Minimum1
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:21.259278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q128
median56
Q385
95-th percentile126
Maximum159
Range158
Interquartile range (IQR)57

Descriptive statistics

Standard deviation36.67952123
Coefficient of variation (CV)0.6249066585
Kurtosis-0.6774781582
Mean58.696
Median Absolute Deviation (MAD)28
Skewness0.4054638236
Sum117392
Variance1345.387278
MonotonicityNot monotonic
2024-03-25T13:34:21.354579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 30
 
1.5%
72 27
 
1.4%
77 27
 
1.4%
30 26
 
1.3%
39 26
 
1.3%
26 26
 
1.3%
20 24
 
1.2%
46 24
 
1.2%
89 23
 
1.1%
75 23
 
1.1%
Other values (143) 1744
87.2%
ValueCountFrequency (%)
1 3
 
0.1%
2 18
0.9%
3 30
1.5%
4 19
0.9%
5 22
1.1%
ValueCountFrequency (%)
159 1
 
0.1%
153 1
 
0.1%
152 1
 
0.1%
151 1
 
0.1%
150 4
0.2%

n_Lefts
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4855
Minimum0
Maximum6
Zeros1367
Zeros (%)68.3%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:21.438981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8456638361
Coefficient of variation (CV)1.741841063
Kurtosis4.291039242
Mean0.4855
Median Absolute Deviation (MAD)0
Skewness2.000391911
Sum971
Variance0.7151473237
MonotonicityNot monotonic
2024-03-25T13:34:21.505597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1367
68.3%
1 394
 
19.7%
2 163
 
8.2%
3 57
 
2.9%
4 16
 
0.8%
5 2
 
0.1%
6 1
 
0.1%
ValueCountFrequency (%)
0 1367
68.3%
1 394
 
19.7%
2 163
 
8.2%
3 57
 
2.9%
4 16
 
0.8%
ValueCountFrequency (%)
6 1
 
0.1%
5 2
 
0.1%
4 16
 
0.8%
3 57
 
2.9%
2 163
8.2%

n_Rights
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.468
Minimum0
Maximum5
Zeros1291
Zeros (%)64.5%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:21.575371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7424632151
Coefficient of variation (CV)1.586459861
Kurtosis4.409814223
Mean0.468
Median Absolute Deviation (MAD)0
Skewness1.89376718
Sum936
Variance0.5512516258
MonotonicityNot monotonic
2024-03-25T13:34:21.645864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1291
64.5%
1 542
27.1%
2 120
 
6.0%
3 36
 
1.8%
4 9
 
0.4%
5 2
 
0.1%
ValueCountFrequency (%)
0 1291
64.5%
1 542
27.1%
2 120
 
6.0%
3 36
 
1.8%
4 9
 
0.4%
ValueCountFrequency (%)
5 2
 
0.1%
4 9
 
0.4%
3 36
 
1.8%
2 120
 
6.0%
1 542
27.1%

Distance2Head
Real number (ℝ)

ZEROS 

Distinct40
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.19
Minimum-26
Maximum23
Zeros126
Zeros (%)6.3%
Negative866
Negative (%)43.3%
Memory size31.2 KiB
2024-03-25T13:34:21.726389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-26
5-th percentile-5
Q1-2
median1
Q31
95-th percentile4
Maximum23
Range49
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.600608586
Coefficient of variation (CV)-18.95057151
Kurtosis11.55671828
Mean-0.19
Median Absolute Deviation (MAD)2
Skewness-0.5953086172
Sum-380
Variance12.96438219
MonotonicityNot monotonic
2024-03-25T13:34:21.813016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 637
31.9%
-1 351
17.5%
-2 233
 
11.7%
2 194
 
9.7%
0 126
 
6.3%
-3 111
 
5.5%
3 68
 
3.4%
-4 57
 
2.9%
4 37
 
1.8%
-5 28
 
1.4%
Other values (30) 158
 
7.9%
ValueCountFrequency (%)
-26 1
 
0.1%
-24 3
0.1%
-19 2
0.1%
-18 3
0.1%
-17 2
0.1%
ValueCountFrequency (%)
23 2
 
0.1%
21 1
 
0.1%
18 1
 
0.1%
17 3
0.1%
16 5
0.2%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:21.881693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.504
Min length4

Characters and Unicode

Total characters9008
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRIGHT
2nd rowLEFT
3rd rowRIGHT
4th rowLEFT
5th rowRIGHT
ValueCountFrequency (%)
right 1008
50.4%
left 866
43.3%
self 126
 
6.3%
2024-03-25T13:34:22.036701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1874
20.8%
R 1008
11.2%
I 1008
11.2%
G 1008
11.2%
H 1008
11.2%
L 992
11.0%
E 992
11.0%
F 992
11.0%
S 126
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9008
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1874
20.8%
R 1008
11.2%
I 1008
11.2%
G 1008
11.2%
H 1008
11.2%
L 992
11.0%
E 992
11.0%
F 992
11.0%
S 126
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 9008
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1874
20.8%
R 1008
11.2%
I 1008
11.2%
G 1008
11.2%
H 1008
11.2%
L 992
11.0%
E 992
11.0%
F 992
11.0%
S 126
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1874
20.8%
R 1008
11.2%
I 1008
11.2%
G 1008
11.2%
H 1008
11.2%
L 992
11.0%
E 992
11.0%
F 992
11.0%
S 126
 
1.4%

prev_Wordfreq_Frequency
Real number (ℝ)

Distinct379
Distinct (%)19.1%
Missing19
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean10.94688169
Minimum4.218934102
Maximum36.54120904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:22.136186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.218934102
5-th percentile4.218934102
Q16.942528932
median9.965784285
Q313.75365078
95-th percentile18.83395777
Maximum36.54120904
Range32.32227494
Interquartile range (IQR)6.811121845

Descriptive statistics

Standard deviation5.392859201
Coefficient of variation (CV)0.4926388497
Kurtosis5.961773842
Mean10.94688169
Median Absolute Deviation (MAD)3.350497247
Skewness1.809996478
Sum21685.77262
Variance29.08293036
MonotonicityNot monotonic
2024-03-25T13:34:22.225886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.218934102 106
 
5.3%
5.316168826 68
 
3.4%
5.448508591 55
 
2.8%
5.28208783 54
 
2.7%
6.615287038 52
 
2.6%
5.216250017 50
 
2.5%
5.748553568 47
 
2.4%
6.41734766 33
 
1.7%
7.673002535 31
 
1.6%
36.54120904 28
 
1.4%
Other values (369) 1457
72.9%
ValueCountFrequency (%)
4.218934102 106
5.3%
5.216250017 50
2.5%
5.28208783 54
2.7%
5.316168826 68
3.4%
5.448508591 55
2.8%
ValueCountFrequency (%)
36.54120904 28
1.4%
35.7841858 1
 
0.1%
34.5091082 1
 
0.1%
31.73385412 1
 
0.1%
26.0797296 1
 
0.1%

prev_subtlex_Frequency
Real number (ℝ)

ZEROS 

Distinct705
Distinct (%)35.6%
Missing19
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean10.01460879
Minimum0
Maximum25.56730941
Zeros135
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:22.322682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.395880917
median9.167263872
Q314.0530885
95-th percentile18.6131131
Maximum25.56730941
Range25.56730941
Interquartile range (IQR)7.657207583

Descriptive statistics

Standard deviation5.131206117
Coefficient of variation (CV)0.5123720981
Kurtosis-0.4045381528
Mean10.01460879
Median Absolute Deviation (MAD)3.385153295
Skewness0.1780141717
Sum19838.94001
Variance26.32927621
MonotonicityNot monotonic
2024-03-25T13:34:22.422388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135
 
6.8%
5.048944398 106
 
5.3%
6.395880917 68
 
3.4%
5.577522722 55
 
2.8%
6.186248136 54
 
2.7%
5.425888255 50
 
2.5%
6.640237508 47
 
2.4%
6.757092393 33
 
1.7%
6.110319383 29
 
1.5%
6.996056719 23
 
1.1%
Other values (695) 1381
69.0%
ValueCountFrequency (%)
0 135
6.8%
1.987142786 1
 
0.1%
4.54169872 3
 
0.1%
4.597300235 1
 
0.1%
4.60821236 8
 
0.4%
ValueCountFrequency (%)
25.56730941 2
0.1%
24.56730941 1
0.1%
23.56730941 2
0.1%
23.24538131 1
0.1%
22.98234691 2
0.1%

prev_Length
Real number (ℝ)

Distinct16
Distinct (%)0.8%
Missing19
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean4.557294296
Minimum0
Maximum16
Zeros12
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:22.506196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q36
95-th percentile9
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.527300142
Coefficient of variation (CV)0.5545615398
Kurtosis0.8632295319
Mean4.557294296
Median Absolute Deviation (MAD)2
Skewness1.009063177
Sum9028
Variance6.387246009
MonotonicityNot monotonic
2024-03-25T13:34:22.581552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 371
18.6%
2 364
18.2%
4 354
17.7%
5 234
11.7%
6 181
9.0%
7 135
 
6.8%
9 87
 
4.3%
8 80
 
4.0%
1 65
 
3.2%
10 46
 
2.3%
Other values (6) 64
 
3.2%
ValueCountFrequency (%)
0 12
 
0.6%
1 65
 
3.2%
2 364
18.2%
3 371
18.6%
4 354
17.7%
ValueCountFrequency (%)
16 2
 
0.1%
14 4
 
0.2%
13 6
 
0.3%
12 16
0.8%
11 24
1.2%

prev_gpt2_Surprisal
Real number (ℝ)

Distinct1923
Distinct (%)97.1%
Missing19
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean5.98433426
Minimum0.0003485667112
Maximum36.63882613
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:22.677426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0003485667112
5-th percentile0.2820212543
Q12.314713001
median4.839929819
Q38.282951474
95-th percentile15.45035093
Maximum36.63882613
Range36.63847757
Interquartile range (IQR)5.968238473

Descriptive statistics

Standard deviation5.064237534
Coefficient of variation (CV)0.8462491087
Kurtosis4.249136376
Mean5.98433426
Median Absolute Deviation (MAD)2.812919617
Skewness1.66958837
Sum11854.96617
Variance25.6465018
MonotonicityNot monotonic
2024-03-25T13:34:22.773484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5930967927 3
 
0.1%
1.304066822 2
 
0.1%
0.419392556 2
 
0.1%
7.814177513 2
 
0.1%
5.524868488 2
 
0.1%
13.1718049 2
 
0.1%
11.4899931 2
 
0.1%
3.271969199 2
 
0.1%
2.863052845 2
 
0.1%
6.874744892 2
 
0.1%
Other values (1913) 1960
98.0%
(Missing) 19
 
0.9%
ValueCountFrequency (%)
0.0003485667112 1
0.1%
0.0009165482479 1
0.1%
0.001493583783 1
0.1%
0.001639438677 1
0.1%
0.002849859651 1
0.1%
ValueCountFrequency (%)
36.63882613 1
0.1%
35.21786308 1
0.1%
33.76476145 1
0.1%
32.59053326 1
0.1%
31.51561904 1
0.1%

regression_rate
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)1.1%
Missing863
Missing (%)43.1%
Infinite0
Infinite (%)0.0%
Mean0.2126879424
Minimum0
Maximum1
Zeros808
Zeros (%)40.4%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:22.862601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.3650806688
Coefficient of variation (CV)1.716508537
Kurtosis0.2959343759
Mean0.2126879424
Median Absolute Deviation (MAD)0
Skewness1.390848784
Sum241.8261905
Variance0.1332838947
MonotonicityNot monotonic
2024-03-25T13:34:22.934352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 808
40.4%
1 164
 
8.2%
0.5 103
 
5.1%
0.3333333333 31
 
1.6%
0.6666666667 15
 
0.8%
0.25 7
 
0.4%
0.4 2
 
0.1%
0.75 2
 
0.1%
0.2 2
 
0.1%
0.1428571429 1
 
0.1%
Other values (2) 2
 
0.1%
(Missing) 863
43.1%
ValueCountFrequency (%)
0 808
40.4%
0.1428571429 1
 
0.1%
0.2 2
 
0.1%
0.25 7
 
0.4%
0.3333333333 31
 
1.6%
ValueCountFrequency (%)
1 164
8.2%
0.8 1
 
0.1%
0.75 2
 
0.1%
0.6666666667 15
 
0.8%
0.6 1
 
0.1%

total_skip
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
False
1137 
True
863 
ValueCountFrequency (%)
False 1137
56.9%
True 863
43.1%
2024-03-25T13:34:23.018191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

part_length
Real number (ℝ)

Distinct127
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.1515
Minimum1
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:23.098728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19
Q135.75
median53
Q375
95-th percentile112
Maximum148
Range147
Interquartile range (IQR)39.25

Descriptive statistics

Standard deviation28.18783115
Coefficient of variation (CV)0.4932124467
Kurtosis-0.1180408468
Mean57.1515
Median Absolute Deviation (MAD)19
Skewness0.6512708194
Sum114303
Variance794.5538247
MonotonicityNot monotonic
2024-03-25T13:34:23.192259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 48
 
2.4%
40 43
 
2.1%
39 41
 
2.1%
64 40
 
2.0%
32 37
 
1.8%
52 36
 
1.8%
37 35
 
1.8%
33 34
 
1.7%
53 34
 
1.7%
70 32
 
1.6%
Other values (117) 1620
81.0%
ValueCountFrequency (%)
1 2
0.1%
2 1
 
0.1%
7 1
 
0.1%
8 3
0.1%
9 2
0.1%
ValueCountFrequency (%)
148 2
 
0.1%
142 7
0.4%
137 1
 
0.1%
136 1
 
0.1%
134 3
0.1%

normalized_dwell_time
Real number (ℝ)

ZEROS 

Distinct1138
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.009120406946
Minimum0
Maximum0.1580327869
Zeros863
Zeros (%)43.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:23.292196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0058560099
Q30.01433565651
95-th percentile0.03219118934
Maximum0.1580327869
Range0.1580327869
Interquartile range (IQR)0.01433565651

Descriptive statistics

Standard deviation0.01220303768
Coefficient of variation (CV)1.337992674
Kurtosis17.03032761
Mean0.009120406946
Median Absolute Deviation (MAD)0.0058560099
Skewness2.757367452
Sum18.24081389
Variance0.0001489141286
MonotonicityNot monotonic
2024-03-25T13:34:23.387671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 863
43.1%
0.008464566929 1
 
0.1%
0.01566324033 1
 
0.1%
0.03152364273 1
 
0.1%
0.02102704987 1
 
0.1%
0.05749884633 1
 
0.1%
0.007935890454 1
 
0.1%
0.01661928712 1
 
0.1%
0.008516568597 1
 
0.1%
0.02931821325 1
 
0.1%
Other values (1128) 1128
56.4%
ValueCountFrequency (%)
0 863
43.1%
0.001059353206 1
 
0.1%
0.001365865773 1
 
0.1%
0.001466911152 1
 
0.1%
0.001757256256 1
 
0.1%
ValueCountFrequency (%)
0.1580327869 1
0.1%
0.1091291961 1
0.1%
0.0975202781 1
0.1%
0.08256382401 1
0.1%
0.07793952968 1
0.1%

normalized_part_dwell_time
Real number (ℝ)

ZEROS 

Distinct1135
Distinct (%)56.9%
Missing5
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.02367776905
Minimum0
Maximum1
Zeros858
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:23.492637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0110483673
Q30.03239891264
95-th percentile0.08638896003
Maximum1
Range1
Interquartile range (IQR)0.03239891264

Descriptive statistics

Standard deviation0.04706557507
Coefficient of variation (CV)1.987753786
Kurtosis189.3082028
Mean0.02367776905
Median Absolute Deviation (MAD)0.0110483673
Skewness10.32635265
Sum47.23714926
Variance0.002215168357
MonotonicityNot monotonic
2024-03-25T13:34:23.591070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 858
42.9%
0.03841931943 2
 
0.1%
1 2
 
0.1%
0.05533199195 2
 
0.1%
0.02950221026 1
 
0.1%
0.01653051418 1
 
0.1%
0.03694083694 1
 
0.1%
0.04096122338 1
 
0.1%
0.09458174905 1
 
0.1%
0.07461077844 1
 
0.1%
Other values (1125) 1125
56.2%
(Missing) 5
 
0.2%
ValueCountFrequency (%)
0 858
42.9%
0.002461780343 1
 
0.1%
0.002907098339 1
 
0.1%
0.002941176471 1
 
0.1%
0.003023016146 1
 
0.1%
ValueCountFrequency (%)
1 2
0.1%
0.3115190998 1
0.1%
0.3035714286 1
0.1%
0.3034901366 1
0.1%
0.3025345622 1
0.1%

normalized_part_ID
Real number (ℝ)

ZEROS 

Distinct1098
Distinct (%)55.0%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.5030863131
Minimum0
Maximum1
Zeros53
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:23.689761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03414457482
Q10.245964691
median0.5138996139
Q30.7631578947
95-th percentile0.966707989
Maximum1
Range1
Interquartile range (IQR)0.5171932037

Descriptive statistics

Standard deviation0.2996734521
Coefficient of variation (CV)0.5956700557
Kurtosis-1.200449317
Mean0.5030863131
Median Absolute Deviation (MAD)0.2578207847
Skewness-0.01038467504
Sum1005.166454
Variance0.08980417792
MonotonicityNot monotonic
2024-03-25T13:34:23.790030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 54
 
2.7%
0 53
 
2.6%
0.3333333333 21
 
1.1%
0.5 18
 
0.9%
0.6666666667 16
 
0.8%
0.25 12
 
0.6%
0.6 11
 
0.5%
0.8 10
 
0.5%
0.2727272727 10
 
0.5%
0.125 9
 
0.4%
Other values (1088) 1784
89.2%
ValueCountFrequency (%)
0 53
2.6%
0.008547008547 1
 
0.1%
0.008928571429 1
 
0.1%
0.0125 1
 
0.1%
0.01587301587 2
 
0.1%
ValueCountFrequency (%)
1 54
2.7%
0.9923076923 1
 
0.1%
0.9903846154 1
 
0.1%
0.9893617021 1
 
0.1%
0.9875 1
 
0.1%

reverse_ID
Real number (ℝ)

ZEROS 

Distinct154
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-56.505
Minimum-162
Maximum0
Zeros21
Zeros (%)1.1%
Negative1979
Negative (%)99.0%
Memory size31.2 KiB
2024-03-25T13:34:23.889511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-162
5-th percentile-123
Q1-83
median-54
Q3-25
95-th percentile-5
Maximum0
Range162
Interquartile range (IQR)58

Descriptive statistics

Standard deviation37.08379132
Coefficient of variation (CV)-0.6562922099
Kurtosis-0.6410866945
Mean-56.505
Median Absolute Deviation (MAD)29
Skewness-0.4369673298
Sum-113010
Variance1375.207579
MonotonicityNot monotonic
2024-03-25T13:34:23.983829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-15 27
 
1.4%
-9 27
 
1.4%
-22 27
 
1.4%
-69 26
 
1.3%
-12 26
 
1.3%
-55 23
 
1.1%
-6 23
 
1.1%
-31 23
 
1.1%
-19 22
 
1.1%
-17 22
 
1.1%
Other values (144) 1754
87.7%
ValueCountFrequency (%)
-162 1
 
0.1%
-161 2
0.1%
-158 1
 
0.1%
-155 3
0.1%
-154 1
 
0.1%
ValueCountFrequency (%)
0 21
1.1%
-1 20
1.0%
-2 19
0.9%
-3 18
0.9%
-4 17
0.9%

reverse_part_ID
Real number (ℝ)

ZEROS 

Distinct116
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-27.89
Minimum-131
Maximum0
Zeros56
Zeros (%)2.8%
Negative1944
Negative (%)97.2%
Memory size31.2 KiB
2024-03-25T13:34:24.084722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-131
5-th percentile-74
Q1-40
median-23
Q3-10
95-th percentile-1
Maximum0
Range131
Interquartile range (IQR)30

Descriptive statistics

Standard deviation23.40407604
Coefficient of variation (CV)-0.8391565451
Kurtosis1.543487119
Mean-27.89
Median Absolute Deviation (MAD)14
Skewness-1.24681341
Sum-55780
Variance547.7507754
MonotonicityNot monotonic
2024-03-25T13:34:24.469703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56
 
2.8%
-11 55
 
2.8%
-2 53
 
2.6%
-4 52
 
2.6%
-3 52
 
2.6%
-1 51
 
2.5%
-14 50
 
2.5%
-5 48
 
2.4%
-6 46
 
2.3%
-15 46
 
2.3%
Other values (106) 1491
74.6%
ValueCountFrequency (%)
-131 1
0.1%
-123 1
0.1%
-119 1
0.1%
-118 2
0.1%
-117 1
0.1%
ValueCountFrequency (%)
0 56
2.8%
-1 51
2.5%
-2 53
2.6%
-3 52
2.6%
-4 52
2.6%

part_ID
Real number (ℝ)

Distinct113
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.2615
Minimum1
Maximum131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:24.567780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q111
median24
Q341
95-th percentile76
Maximum131
Range130
Interquartile range (IQR)30

Descriptive statistics

Standard deviation23.29799382
Coefficient of variation (CV)0.7961995735
Kurtosis1.375277836
Mean29.2615
Median Absolute Deviation (MAD)14
Skewness1.190128867
Sum58523
Variance542.796516
MonotonicityNot monotonic
2024-03-25T13:34:24.667663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 58
 
2.9%
1 55
 
2.8%
2 52
 
2.6%
18 52
 
2.6%
4 50
 
2.5%
26 50
 
2.5%
3 49
 
2.5%
15 48
 
2.4%
10 45
 
2.2%
16 43
 
2.1%
Other values (103) 1498
74.9%
ValueCountFrequency (%)
1 55
2.8%
2 52
2.6%
3 49
2.5%
4 50
2.5%
5 43
2.1%
ValueCountFrequency (%)
131 1
0.1%
130 2
0.1%
123 1
0.1%
122 1
0.1%
121 1
0.1%

normalized_ID
Real number (ℝ)

ZEROS 

Distinct1580
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5074658472
Minimum0
Maximum1
Zeros22
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:24.769390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04159946237
Q10.2529575299
median0.5064113102
Q30.7687539733
95-th percentile0.9548495832
Maximum1
Range1
Interquartile range (IQR)0.5157964434

Descriptive statistics

Standard deviation0.2925545402
Coefficient of variation (CV)0.5765009445
Kurtosis-1.216561188
Mean0.5074658472
Median Absolute Deviation (MAD)0.2583219334
Skewness-0.03221522019
Sum1014.931694
Variance0.08558815899
MonotonicityNot monotonic
2024-03-25T13:34:24.868427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
 
1.1%
1 21
 
1.1%
0.3333333333 11
 
0.5%
0.6666666667 11
 
0.5%
0.5 8
 
0.4%
0.1428571429 6
 
0.3%
0.4166666667 5
 
0.2%
0.2231404959 4
 
0.2%
0.2857142857 4
 
0.2%
0.6 4
 
0.2%
Other values (1570) 1904
95.2%
ValueCountFrequency (%)
0 22
1.1%
0.006134969325 1
 
0.1%
0.006535947712 1
 
0.1%
0.006578947368 2
 
0.1%
0.007352941176 1
 
0.1%
ValueCountFrequency (%)
1 21
1.1%
0.9931972789 1
 
0.1%
0.992481203 1
 
0.1%
0.9923076923 1
 
0.1%
0.9919354839 1
 
0.1%

cs_has_two_questions
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.663
Minimum0
Maximum1
Zeros674
Zeros (%)33.7%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:24.947846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4728031
Coefficient of variation (CV)0.7131268477
Kurtosis-1.525157047
Mean0.663
Median Absolute Deviation (MAD)0
Skewness-0.6901949804
Sum1326
Variance0.2235427714
MonotonicityNot monotonic
2024-03-25T13:34:25.015597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 1326
66.3%
0 674
33.7%
ValueCountFrequency (%)
0 674
33.7%
1 1326
66.3%
ValueCountFrequency (%)
1 1326
66.3%
0 674
33.7%

q_reference
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.485
Minimum0
Maximum1
Zeros1030
Zeros (%)51.5%
Negative0
Negative (%)0.0%
Memory size31.2 KiB
2024-03-25T13:34:25.083443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.49989994
Coefficient of variation (CV)1.030721526
Kurtosis-1.998390737
Mean0.485
Median Absolute Deviation (MAD)0
Skewness0.06007208169
Sum970
Variance0.24989995
MonotonicityNot monotonic
2024-03-25T13:34:25.149728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 1030
51.5%
1 970
48.5%
ValueCountFrequency (%)
0 1030
51.5%
1 970
48.5%
ValueCountFrequency (%)
1 970
48.5%
0 1030
51.5%